Dimkid analysis
Kara Weisman
1/22/2017
Preliminaries
Set up environment
library(tidyverse)
library(langcog)
library(stats)
library(psych)
library(GPArotation)
library(lme4)
library(RColorBrewer)
# clear environment
rm(list=ls())
graphics.off()Read in data
# ADULT run 01 (3-point scale)
d_adult01 <- read.csv("/Users/kweisman/Documents/Research (Stanford)/Projects/Dimkid/dimkid/data/adults/us_run-01_2016-06-05_anonymized.csv") %>% select(-X)
# # ADULT run 02 (7-point scale)
# d_adult02 <- read.csv("/Users/kweisman/Documents/Research (Stanford)/Projects/Dimkid/dimkid/data/adults/us_run-02_2016-07-19_anonymized.csv") %>% select(-X)
#
# # ADULT run 03 (3-point scale, original wording for 'free will' and 'intentions')
# d_adult03 <- read.csv("/Users/kweisman/Documents/Research (Stanford)/Projects/Dimkid/dimkid/data/adults/us_run-03_2016-12-08_anonymized.csv") %>% select(-X)
# CHILD run 01 [lydia, olivia, allie (summer 2016) + nicky, dru, ariel, olivia (fall 2016) + campbell (winter 2017)]
d_child01 <- read.csv("/Users/kweisman/Documents/Research (Stanford)/Projects/Dimkid/dimkid/data/children/run-01_2017-01-19_anonymized.csv") %>% select(-X)
# demographic info (for children)
child_demo <- read.csv("/Users/kweisman/Documents/Research (Stanford)/Projects/Dimkid/dimkid/data/children/dimkid_participant_ages_2017-01-19.csv")Tidy data
# tidy adult run 01
d_adult02 <- d_adult01 %>%
select(subid, age, gender, ethnicity, duration,
charName, trialNum, capacity, capWording,
hoverTime, rt, response, responseNum) %>%
rename(character = charName,
sessionDuration = duration) %>%
mutate(ageGroup = "adult")
# tidy demographics
child_demo_tidy <- child_demo %>%
mutate(subid = gsub(" ", "", subid)) %>%
mutate(subid = gsub("-", "", subid)) %>%
mutate(subid = toupper(subid)) %>%
select(subid, age, gender, ethnicity)
# tidy child run 01
d_child02 <- d_child01 %>%
filter(character %in% c("beetle", "robot")) %>% # no elephant condition! (n = 1 child)
select(subid, sessionDuration,
character, trialNum, capacity, capWording,
hoverTime, rt, response, responseNum) %>%
mutate(ageGroup = "child") %>%
mutate(subid = gsub(" ", "", subid)) %>%
mutate(subid = gsub("-", "", subid)) %>%
mutate(subid = toupper(subid)) %>%
left_join(child_demo_tidy) %>%
mutate(subid = factor(as.character(subid)))Joining, by = "subid"# combine datasets
d <- d_adult02 %>%
full_join(d_child02) %>%
mutate(subid = factor(subid),
character = factor(character),
capWording = factor(gsub("\\ ", "_", gsub(" --.*", "", capWording))),
responseCat = factor(response, levels = c("no", "kinda", "yes")),
responseNum = ifelse(response == "no", 0,
ifelse(response == "kinda", 0.5,
ifelse(response == "yes", 1,
NA))),
ageGroup = factor(ageGroup, levels = c("adult", "child"))) %>%
distinct()Joining, by = c("subid", "age", "gender", "ethnicity", "sessionDuration", "character", "trialNum", "capacity", "capWording", "hoverTime", "rt", "response", "responseNum", "ageGroup")
joining factors with different levels, coercing to character vectorjoining factors with different levels, coercing to character vectorjoining factors with different levels, coercing to character vectorjoining factors with different levels, coercing to character vectorjoining factors with different levels, coercing to character vectorjoining factors with different levels, coercing to character vectorDemographics
# total n
d %>%
select(ageGroup, subid, age) %>%
distinct(.keep_all = T) %>%
mutate(ageGroup2 = ifelse(ageGroup == "adult",
"adult",
ifelse(age < 7 | age > 10,
"out of age range",
ifelse(is.na(age),
"missing age",
"child")))) %>%
count(ageGroup2)# drop children outside of target age range (7-9y)
d1 <- d %>%
filter((age >= 7 & age <= 10) | ageGroup == "adult")
d1 %>%
select(ageGroup, subid, age) %>%
distinct(.keep_all = T) %>%
mutate(ageGroup2 = ifelse(ageGroup == "adult",
"adult",
ifelse(age < 7 | age > 10,
"out of age range",
ifelse(is.na(age),
"missing age",
"child")))) %>%
count(ageGroup2)# condition
d1 %>%
select(ageGroup, subid, character) %>%
distinct(.keep_all = T) %>%
count(ageGroup, character)# age
d1 %>%
select(ageGroup, subid, age) %>%
distinct(.keep_all = T) %>%
group_by(ageGroup) %>%
summarise(mean_age = mean(age, na.rm = T),
sd_age = sd(age, na.rm = T),
median_age = median(age, na.rm = T),
min_age = min(age, na.rm = T),
max_age = max(age, na.rm = T))# plot children's age
qplot(d1 %>%
filter(ageGroup == "child") %>%
distinct(subid, .keep_all = T) %>%
select(age), bins = 18) +
geom_vline(xintercept = median(d1$age[d1$ageGroup == "child"], na.rm = T), color = "red")
# check children's age by condition
d1 %>%
filter(ageGroup == "child") %>%
distinct(subid, .keep_all = T) %>%
select(age, character) %>%
group_by(character) %>%
summarise(median = median(age, na.rm = T))t.test(age ~ character,
d1 %>%
filter(ageGroup == "child") %>%
select(subid, age, character) %>%
distinct)
Welch Two Sample t-test
data: age by character
t = 0.48254, df = 197.76, p-value = 0.63
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.1769700 0.2916335
sample estimates:
mean in group beetle mean in group robot
8.381464 8.324132 ggplot(d1 %>%
filter(ageGroup == "child") %>%
distinct(subid, .keep_all = T) %>%
select(age, character) %>%
group_by(character) %>%
mutate(median_age = median(age, na.rm = T)),
aes(x = age)) +
geom_histogram(bins = 9) +
facet_wrap(~ character) +
geom_vline(xintercept = median(d$age[d$ageGroup == "child"], na.rm = T), color = "black") +
geom_vline(aes(xintercept = median_age, color = character), lty = 2)
# duration
d1 %>%
group_by(ageGroup) %>%
summarise(mean = mean(sessionDuration, na.rm = T),
median = median(sessionDuration, na.rm = T),
min = min(sessionDuration, na.rm = T),
max = max(sessionDuration, na.rm = T))# plot duration by age group and character
ggplot(d1, aes(sessionDuration)) +
facet_grid(character ~ ageGroup) +
geom_histogram(breaks = 0:15)
t.test(sessionDuration ~ character,
d1 %>%
filter(ageGroup == "child") %>%
select(subid, character, sessionDuration) %>%
left_join(d %>% select(subid, sessionDuration)) %>%
mutate(subid = as.character(subid),
character = factor(character),
sessionDuration = round(as.numeric(as.character(sessionDuration)), 3)) %>%
distinct())Joining, by = c("subid", "sessionDuration")
Joining, by = c("subid", "sessionDuration")
Welch Two Sample t-test
data: sessionDuration by character
t = -0.38762, df = 149.35, p-value = 0.6988
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.5105418 0.3430885
sample estimates:
mean in group beetle mean in group robot
5.293337 5.377063 t.test(sessionDuration ~ character,
d1 %>%
filter(ageGroup == "adult") %>%
select(subid, character, sessionDuration) %>%
left_join(d %>% select(subid, sessionDuration)) %>%
mutate(subid = as.character(subid),
character = factor(character),
sessionDuration = round(as.numeric(as.character(sessionDuration)), 3)) %>%
distinct())Joining, by = c("subid", "sessionDuration")
Joining, by = c("subid", "sessionDuration")
Welch Two Sample t-test
data: sessionDuration by character
t = 0.76334, df = 186.45, p-value = 0.4462
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.2954407 0.6683779
sample estimates:
mean in group beetle mean in group robot
2.963449 2.776980 # gender
d1 %>%
select(ageGroup, subid, gender) %>%
distinct(.keep_all = T) %>%
count(ageGroup, gender)# ethnicity
d1 %>%
select(ageGroup, subid, ethnicity) %>%
mutate(ethnicity = tolower(ethnicity)) %>%
mutate(black = grepl("black", ethnicity) |
grepl("african american", ethnicity),
east_asian = grepl("east asian", ethnicity) |
grepl("eastasian", ethnicity) |
grepl("chinese", ethnicity) |
grepl("china", ethnicity) |
grepl("korea", ethnicity) |
grepl("japan", ethnicity) |
grepl("taiwan", ethnicity),
south_asian = grepl("south asian", ethnicity) |
grepl("southasian", ethnicity) |
grepl("india", ethnicity) |
grepl("pakistan", ethnicity) |
grepl("bangla", ethnicity) |
grepl("sri lanka", ethnicity),
latino = grepl("latin", ethnicity) |
grepl("hispanic", ethnicity) |
grepl("mexic", ethnicity),
middle_eastern = grepl("middle", ethnicity),
pac_island = grepl("pacific", ethnicity) |
grepl("hawaii", ethnicity),
native_am = grepl("nativeamerican", ethnicity) |
grepl("native american", ethnicity) |
grepl("american indian", ethnicity) |
grepl("alaska native", ethnicity),
white = grepl("white", ethnicity),
other = grepl("other", ethnicity)) %>%
distinct(.keep_all = T) %>%
gather(ethnicityTF, TF, -subid, -ethnicity, -ageGroup) %>%
filter(TF) %>%
count(ageGroup, ethnicityTF)# NOTE: not mutually exclusive!!
# mutually exclusive version
d1 %>%
select(ageGroup, subid, ethnicity) %>%
mutate(ethnicity = tolower(ethnicity)) %>%
distinct(.keep_all = T) %>%
mutate(ethnicity2 = ifelse(ageGroup == "adult",
ifelse(grepl(",", ethnicity), "multiple", ethnicity),
ifelse(grepl(";", ethnicity), "multiple", ethnicity))) %>%
mutate(ethnicity3 = ifelse(grepl("other", ethnicity2),
ifelse(grepl("/", ethnicity2) | grepl("mix", ethnicity2),
"multiple",
ifelse(grepl("asian", ethnicity2) | grepl("korean", ethnicity2),
"east asian",
ethnicity2)),
ethnicity2)) %>%
count(ageGroup, ethnicity3) %>%
ungroup() %>%
group_by(ageGroup) %>%
mutate(prop = n/sum(n)) %>%
arrange(ageGroup, desc(n))# experimineter error
d_child01 %>% # use d_child01 (before dropping n = 1 child in "elephant" condition)
filter(!(character %in% c("beetle", "robot"))) %>%
select(subid) %>%
distinct() %>%
count() %>%
mutate(percent = n/200)# incomplete trials ("skip", "bail")
d1 %>% # use d1 (before dropping trials with rt > 250ms)
count(ageGroup, subid) %>%
filter(n != 40) %>%
mutate(n_missing = 40-n) %>%
group_by(ageGroup) %>%
summarise(sum_missing = sum(n_missing)) %>%
mutate(percent = sum_missing/8000)# trials with rt > 250ms
d1 %>%
filter(rt < 250) %>%
count(ageGroup) %>%
mutate(percent = n/8000)# all missing trials
d1 %>% # use d1 (before dropping trials)
count(ageGroup, subid) %>%
filter(n != 40) %>%
mutate(n_NA = 40-n) %>%
group_by(ageGroup) %>%
summarise(n_NA = sum(n_NA)) %>%
mutate(percent_NA = n_NA/8000) %>%
full_join(d1 %>%
filter(rt < 250) %>%
count(ageGroup) %>%
rename(n_fast = n) %>%
mutate(percent_fast = n_fast/8000)) %>%
mutate(n_missing_TOTAL = n_NA + n_fast,
percent_missing_TOTAL = percent_NA + percent_fast)Joining, by = "ageGroup"NAData preparation
Filter RTs
# examine and filter by RTs
ggplot(d1) +
geom_histogram(aes(x = rt), bins = 100) +
facet_wrap(~ageGroup) +
scale_x_log10(breaks = seq(0, 1000, 100)) +
geom_vline(xintercept = 250, color = "red")
d2 <- d1 %>%
filter(!(rt < 250) | is.na(rt)) # preset criterion of 250msLook at hover time
d2 %>%
filter(!(capacity %in% c("personality", "beliefs", "pleasure", "desires", "self_restraint", "goals", "nauseated"))) %>%
mutate(noHover = hoverTime == 0) %>%
count(ageGroup, noHover) %>%
group_by(ageGroup) %>%
mutate(percent = n/sum(n))ggplot(d2, aes(x = hoverTime)) +
facet_grid(~ ageGroup) +
geom_histogram()
Prepare data for EFA
# finish tidying
d3 <- d2 %>%
select(capacity, responseNum, subid) %>%
spread(capacity, responseNum)
# make combined dataset
d3_combined <- data.frame(d3[,-1], row.names = d3[,1])
# make separate child and adult datasets
d3_adult <- d3 %>%
left_join(d %>% select(subid, ageGroup)) %>%
filter(ageGroup == "adult") %>%
select(-ageGroup) %>%
distinct()Joining, by = "subid"d3_adult <- data.frame(d3_adult[,-1], row.names = d3_adult[,1])
d3_child <- d3 %>%
left_join(d %>% select(subid, ageGroup)) %>%
filter(ageGroup == "child") %>%
select(-ageGroup) %>%
distinct()Joining, by = "subid"d3_child <- data.frame(d3_child[,-1], row.names = d3_child[,1])General analysis settings
# set correlation type: pearson or polychoric?
cor_type <- "cor"
# cor_type <- "poly"
# set rotation type: varimax or oblimin?
rot_type <- "varimax"
# rot_type <- "oblimin"
# set score type
# score_type <- "regression"
# score_type <- "Thurstone"
score_type <- "tenBerge"
# score_type <- "Anderson"
# score_type <- "Bartlett"
# score_type <- "Harman"Adults alone
Exploratory factor analysis
Maximal (13-factor) unrotated solution
# do factor analysis
efa_adult_max_unrot <- fa(d3_adult, nfactors = 13, rotate = "none", cor = cor_type)
efa_adult_max_unrotFactor Analysis using method = minres
Call: fa(r = d3_adult, nfactors = 13, rotate = "none", cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR11 MR9 MR10 MR12 MR13 h2
angry 0.66 0.09 0.03 0.00 -0.02 -0.23 -0.08 -0.13 0.05 0.01 0.04 -0.02 0.21 0.57
beliefs 0.39 0.45 0.16 -0.06 0.23 -0.06 0.44 0.10 0.03 0.13 -0.14 -0.01 -0.01 0.68
calm 0.69 -0.07 0.13 -0.08 0.08 -0.04 -0.04 0.10 0.08 0.10 0.08 0.12 0.05 0.56
choices 0.24 0.22 0.48 0.12 0.25 0.03 -0.20 -0.09 -0.14 -0.19 -0.12 -0.18 0.14 0.59
communicating -0.02 0.51 0.45 0.11 -0.27 -0.14 -0.19 0.16 -0.01 0.10 -0.08 0.08 -0.11 0.67
computations -0.48 0.74 0.20 0.00 -0.14 0.01 0.05 0.02 0.05 -0.05 -0.03 0.00 0.08 0.84
conscious 0.34 0.00 0.42 0.22 0.20 0.07 -0.01 -0.14 0.00 -0.14 -0.15 0.07 0.04 0.45
depressed 0.74 0.27 -0.08 -0.34 -0.01 0.06 -0.09 -0.16 0.14 -0.04 0.02 0.00 -0.05 0.81
depth 0.14 0.14 0.46 0.31 -0.06 0.29 0.02 -0.20 -0.05 -0.01 -0.07 0.01 0.05 0.48
desires 0.64 -0.09 0.14 0.03 0.11 0.07 0.00 0.03 -0.24 0.27 0.11 -0.16 -0.02 0.63
disrespected 0.65 0.31 -0.18 -0.07 -0.01 0.11 -0.07 -0.13 0.03 0.04 -0.04 0.02 -0.06 0.60
embarrassed 0.56 0.44 -0.38 0.32 -0.01 -0.04 -0.02 0.18 0.02 -0.14 0.18 0.05 0.01 0.84
emo_recog 0.27 0.41 0.18 -0.11 0.17 0.04 0.28 0.03 -0.03 -0.17 -0.06 0.16 -0.16 0.49
fear 0.79 -0.35 0.20 -0.06 -0.24 -0.22 0.12 -0.06 -0.03 -0.08 0.02 -0.02 0.00 0.91
free_will 0.19 0.17 0.40 0.16 0.36 -0.19 -0.19 -0.12 -0.14 -0.08 0.07 -0.03 0.09 0.51
goal 0.32 0.16 0.32 0.11 0.13 -0.08 0.18 0.02 -0.10 0.01 0.09 -0.15 0.05 0.35
guilt 0.67 0.48 -0.38 0.31 -0.03 -0.04 0.00 -0.07 -0.02 0.03 -0.09 -0.02 -0.02 0.93
happy 0.76 0.20 -0.02 -0.38 0.04 -0.03 -0.15 -0.08 0.05 0.04 -0.08 0.05 -0.01 0.81
hungry 0.65 -0.64 -0.01 0.15 0.09 0.08 -0.02 0.07 0.00 0.04 0.02 0.01 -0.02 0.89
intentions 0.05 0.54 0.33 0.00 0.13 -0.20 0.06 0.05 0.08 0.05 0.30 -0.13 -0.05 0.59
joy 0.77 0.26 -0.10 -0.33 -0.06 0.14 -0.04 0.09 -0.10 0.02 0.05 0.10 0.05 0.84
love 0.73 0.31 -0.02 -0.19 0.07 0.22 0.04 -0.01 -0.07 0.03 0.01 -0.12 -0.07 0.74
morality 0.20 0.50 0.20 -0.10 0.04 0.03 0.22 0.10 0.09 -0.02 0.05 -0.15 0.01 0.43
nauseated 0.67 -0.26 0.16 -0.03 -0.06 0.13 -0.09 0.11 -0.14 0.07 0.02 -0.02 0.05 0.61
odors 0.50 -0.37 0.20 0.26 0.10 0.19 0.00 0.08 0.03 -0.09 0.06 0.22 0.02 0.61
pain 0.71 -0.45 0.09 0.12 -0.06 -0.08 -0.09 0.13 0.15 0.10 -0.10 -0.02 0.03 0.81
personality 0.35 0.42 0.12 -0.14 0.20 0.03 0.19 0.17 -0.11 0.03 -0.12 0.15 0.11 0.50
pleasure 0.72 -0.10 0.04 -0.12 -0.13 0.23 0.01 0.20 0.01 -0.27 -0.09 -0.13 -0.06 0.76
pride 0.66 0.41 -0.12 -0.19 0.01 -0.02 0.05 -0.08 0.00 0.06 0.19 0.01 0.12 0.72
reasoning 0.21 0.30 0.49 0.12 0.11 -0.15 -0.16 -0.08 -0.20 -0.11 0.01 0.22 -0.06 0.56
recognizing -0.07 0.65 0.47 0.03 -0.19 -0.04 -0.09 0.18 -0.01 0.12 -0.07 0.04 0.03 0.75
remembering -0.01 0.54 0.44 0.04 -0.08 0.05 -0.14 0.05 -0.04 -0.01 0.04 -0.14 -0.24 0.60
safe 0.72 -0.27 0.24 0.03 0.10 -0.11 0.12 -0.09 -0.05 0.00 -0.04 0.08 -0.10 0.72
seeing 0.24 0.03 0.45 0.33 -0.08 0.27 -0.02 -0.15 0.14 -0.02 0.06 -0.01 0.00 0.50
self_aware 0.36 0.11 0.33 0.09 0.31 -0.14 -0.05 -0.01 0.17 0.10 0.05 0.07 -0.22 0.47
self_restraint 0.33 0.36 0.20 -0.07 0.23 0.02 0.09 0.07 0.15 -0.06 -0.09 0.03 0.17 0.42
sounds 0.18 0.09 0.45 0.21 -0.28 0.22 0.06 -0.17 -0.02 0.06 0.18 0.06 0.00 0.48
temperature 0.21 0.06 0.44 0.28 -0.14 0.35 0.17 -0.18 0.17 0.10 0.11 0.04 0.03 0.58
thoughts 0.45 0.15 0.31 -0.06 0.36 -0.11 -0.14 0.13 0.21 -0.10 0.04 -0.09 0.00 0.57
tired 0.71 -0.29 0.20 0.11 -0.07 0.04 -0.03 0.07 0.06 0.07 -0.07 -0.08 0.12 0.69
u2 com
angry 0.427 1.7
beliefs 0.323 4.4
calm 0.445 1.4
choices 0.413 5.1
communicating 0.333 3.8
computations 0.155 2.1
conscious 0.545 4.1
depressed 0.191 2.0
depth 0.515 3.7
desires 0.370 2.2
disrespected 0.403 1.9
embarrassed 0.160 4.2
emo_recog 0.513 5.3
fear 0.087 2.1
free_will 0.492 5.4
goal 0.654 4.9
guilt 0.070 3.1
happy 0.189 1.8
hungry 0.112 2.2
intentions 0.415 3.2
joy 0.162 1.9
love 0.262 1.9
morality 0.570 2.8
nauseated 0.391 1.8
odors 0.391 4.0
pain 0.192 2.2
personality 0.501 4.9
pleasure 0.242 2.0
pride 0.282 2.3
reasoning 0.438 4.3
recognizing 0.251 2.5
remembering 0.396 2.8
safe 0.283 1.8
seeing 0.498 3.9
self_aware 0.529 5.2
self_restraint 0.582 4.9
sounds 0.519 4.4
temperature 0.423 5.2
thoughts 0.433 4.6
tired 0.313 1.8
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR11 MR9 MR10 MR12 MR13
SS loadings 10.58 5.08 3.45 1.31 1.07 0.82 0.73 0.53 0.43 0.41 0.40 0.39 0.34
Proportion Var 0.26 0.13 0.09 0.03 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.26 0.39 0.48 0.51 0.54 0.56 0.58 0.59 0.60 0.61 0.62 0.63 0.64
Proportion Explained 0.41 0.20 0.13 0.05 0.04 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.01
Cumulative Proportion 0.41 0.61 0.75 0.80 0.84 0.87 0.90 0.92 0.94 0.96 0.97 0.99 1.00
Mean item complexity = 3.2
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 27.45 with Chi Square of 5073.12
The degrees of freedom for the model are 338 and the objective function was 2.29
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 196 with the empirical chi square 118.47 with prob < 1
The total number of observations was 200 with MLE Chi Square = 403.84 with prob < 0.008
Tucker Lewis Index of factoring reliability = 0.963
RMSEA index = 0.042 and the 90 % confidence intervals are 0.017 0.042
BIC = -1386.99
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR11 MR9
Correlation of scores with factors 0.99 0.98 0.96 0.93 0.87 0.85 0.82 0.80 0.73 0.76
Multiple R square of scores with factors 0.98 0.96 0.92 0.86 0.76 0.72 0.67 0.64 0.54 0.57
Minimum correlation of possible factor scores 0.96 0.92 0.83 0.72 0.52 0.44 0.34 0.27 0.08 0.15
MR10 MR12 MR13
Correlation of scores with factors 0.75 0.71 0.68
Multiple R square of scores with factors 0.56 0.50 0.47
Minimum correlation of possible factor scores 0.12 0.01 -0.07# examine eigenvalues and variance explained
efa_adult_max_unrot_eigenvalues <- print(efa_adult_max_unrot)$Vaccounted %>%
t() %>%
data.frame()Factor Analysis using method = minres
Call: fa(r = d3_adult, nfactors = 13, rotate = "none", cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR11 MR9 MR10 MR12 MR13 h2
angry 0.66 0.09 0.03 0.00 -0.02 -0.23 -0.08 -0.13 0.05 0.01 0.04 -0.02 0.21 0.57
beliefs 0.39 0.45 0.16 -0.06 0.23 -0.06 0.44 0.10 0.03 0.13 -0.14 -0.01 -0.01 0.68
calm 0.69 -0.07 0.13 -0.08 0.08 -0.04 -0.04 0.10 0.08 0.10 0.08 0.12 0.05 0.56
choices 0.24 0.22 0.48 0.12 0.25 0.03 -0.20 -0.09 -0.14 -0.19 -0.12 -0.18 0.14 0.59
communicating -0.02 0.51 0.45 0.11 -0.27 -0.14 -0.19 0.16 -0.01 0.10 -0.08 0.08 -0.11 0.67
computations -0.48 0.74 0.20 0.00 -0.14 0.01 0.05 0.02 0.05 -0.05 -0.03 0.00 0.08 0.84
conscious 0.34 0.00 0.42 0.22 0.20 0.07 -0.01 -0.14 0.00 -0.14 -0.15 0.07 0.04 0.45
depressed 0.74 0.27 -0.08 -0.34 -0.01 0.06 -0.09 -0.16 0.14 -0.04 0.02 0.00 -0.05 0.81
depth 0.14 0.14 0.46 0.31 -0.06 0.29 0.02 -0.20 -0.05 -0.01 -0.07 0.01 0.05 0.48
desires 0.64 -0.09 0.14 0.03 0.11 0.07 0.00 0.03 -0.24 0.27 0.11 -0.16 -0.02 0.63
disrespected 0.65 0.31 -0.18 -0.07 -0.01 0.11 -0.07 -0.13 0.03 0.04 -0.04 0.02 -0.06 0.60
embarrassed 0.56 0.44 -0.38 0.32 -0.01 -0.04 -0.02 0.18 0.02 -0.14 0.18 0.05 0.01 0.84
emo_recog 0.27 0.41 0.18 -0.11 0.17 0.04 0.28 0.03 -0.03 -0.17 -0.06 0.16 -0.16 0.49
fear 0.79 -0.35 0.20 -0.06 -0.24 -0.22 0.12 -0.06 -0.03 -0.08 0.02 -0.02 0.00 0.91
free_will 0.19 0.17 0.40 0.16 0.36 -0.19 -0.19 -0.12 -0.14 -0.08 0.07 -0.03 0.09 0.51
goal 0.32 0.16 0.32 0.11 0.13 -0.08 0.18 0.02 -0.10 0.01 0.09 -0.15 0.05 0.35
guilt 0.67 0.48 -0.38 0.31 -0.03 -0.04 0.00 -0.07 -0.02 0.03 -0.09 -0.02 -0.02 0.93
happy 0.76 0.20 -0.02 -0.38 0.04 -0.03 -0.15 -0.08 0.05 0.04 -0.08 0.05 -0.01 0.81
hungry 0.65 -0.64 -0.01 0.15 0.09 0.08 -0.02 0.07 0.00 0.04 0.02 0.01 -0.02 0.89
intentions 0.05 0.54 0.33 0.00 0.13 -0.20 0.06 0.05 0.08 0.05 0.30 -0.13 -0.05 0.59
joy 0.77 0.26 -0.10 -0.33 -0.06 0.14 -0.04 0.09 -0.10 0.02 0.05 0.10 0.05 0.84
love 0.73 0.31 -0.02 -0.19 0.07 0.22 0.04 -0.01 -0.07 0.03 0.01 -0.12 -0.07 0.74
morality 0.20 0.50 0.20 -0.10 0.04 0.03 0.22 0.10 0.09 -0.02 0.05 -0.15 0.01 0.43
nauseated 0.67 -0.26 0.16 -0.03 -0.06 0.13 -0.09 0.11 -0.14 0.07 0.02 -0.02 0.05 0.61
odors 0.50 -0.37 0.20 0.26 0.10 0.19 0.00 0.08 0.03 -0.09 0.06 0.22 0.02 0.61
pain 0.71 -0.45 0.09 0.12 -0.06 -0.08 -0.09 0.13 0.15 0.10 -0.10 -0.02 0.03 0.81
personality 0.35 0.42 0.12 -0.14 0.20 0.03 0.19 0.17 -0.11 0.03 -0.12 0.15 0.11 0.50
pleasure 0.72 -0.10 0.04 -0.12 -0.13 0.23 0.01 0.20 0.01 -0.27 -0.09 -0.13 -0.06 0.76
pride 0.66 0.41 -0.12 -0.19 0.01 -0.02 0.05 -0.08 0.00 0.06 0.19 0.01 0.12 0.72
reasoning 0.21 0.30 0.49 0.12 0.11 -0.15 -0.16 -0.08 -0.20 -0.11 0.01 0.22 -0.06 0.56
recognizing -0.07 0.65 0.47 0.03 -0.19 -0.04 -0.09 0.18 -0.01 0.12 -0.07 0.04 0.03 0.75
remembering -0.01 0.54 0.44 0.04 -0.08 0.05 -0.14 0.05 -0.04 -0.01 0.04 -0.14 -0.24 0.60
safe 0.72 -0.27 0.24 0.03 0.10 -0.11 0.12 -0.09 -0.05 0.00 -0.04 0.08 -0.10 0.72
seeing 0.24 0.03 0.45 0.33 -0.08 0.27 -0.02 -0.15 0.14 -0.02 0.06 -0.01 0.00 0.50
self_aware 0.36 0.11 0.33 0.09 0.31 -0.14 -0.05 -0.01 0.17 0.10 0.05 0.07 -0.22 0.47
self_restraint 0.33 0.36 0.20 -0.07 0.23 0.02 0.09 0.07 0.15 -0.06 -0.09 0.03 0.17 0.42
sounds 0.18 0.09 0.45 0.21 -0.28 0.22 0.06 -0.17 -0.02 0.06 0.18 0.06 0.00 0.48
temperature 0.21 0.06 0.44 0.28 -0.14 0.35 0.17 -0.18 0.17 0.10 0.11 0.04 0.03 0.58
thoughts 0.45 0.15 0.31 -0.06 0.36 -0.11 -0.14 0.13 0.21 -0.10 0.04 -0.09 0.00 0.57
tired 0.71 -0.29 0.20 0.11 -0.07 0.04 -0.03 0.07 0.06 0.07 -0.07 -0.08 0.12 0.69
u2 com
angry 0.427 1.7
beliefs 0.323 4.4
calm 0.445 1.4
choices 0.413 5.1
communicating 0.333 3.8
computations 0.155 2.1
conscious 0.545 4.1
depressed 0.191 2.0
depth 0.515 3.7
desires 0.370 2.2
disrespected 0.403 1.9
embarrassed 0.160 4.2
emo_recog 0.513 5.3
fear 0.087 2.1
free_will 0.492 5.4
goal 0.654 4.9
guilt 0.070 3.1
happy 0.189 1.8
hungry 0.112 2.2
intentions 0.415 3.2
joy 0.162 1.9
love 0.262 1.9
morality 0.570 2.8
nauseated 0.391 1.8
odors 0.391 4.0
pain 0.192 2.2
personality 0.501 4.9
pleasure 0.242 2.0
pride 0.282 2.3
reasoning 0.438 4.3
recognizing 0.251 2.5
remembering 0.396 2.8
safe 0.283 1.8
seeing 0.498 3.9
self_aware 0.529 5.2
self_restraint 0.582 4.9
sounds 0.519 4.4
temperature 0.423 5.2
thoughts 0.433 4.6
tired 0.313 1.8
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR11 MR9 MR10 MR12 MR13
SS loadings 10.58 5.08 3.45 1.31 1.07 0.82 0.73 0.53 0.43 0.41 0.40 0.39 0.34
Proportion Var 0.26 0.13 0.09 0.03 0.03 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.26 0.39 0.48 0.51 0.54 0.56 0.58 0.59 0.60 0.61 0.62 0.63 0.64
Proportion Explained 0.41 0.20 0.13 0.05 0.04 0.03 0.03 0.02 0.02 0.02 0.02 0.02 0.01
Cumulative Proportion 0.41 0.61 0.75 0.80 0.84 0.87 0.90 0.92 0.94 0.96 0.97 0.99 1.00
Mean item complexity = 3.2
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 27.45 with Chi Square of 5073.12
The degrees of freedom for the model are 338 and the objective function was 2.29
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 196 with the empirical chi square 118.47 with prob < 1
The total number of observations was 200 with MLE Chi Square = 403.84 with prob < 0.008
Tucker Lewis Index of factoring reliability = 0.963
RMSEA index = 0.042 and the 90 % confidence intervals are 0.017 0.042
BIC = -1386.99
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR11 MR9
Correlation of scores with factors 0.99 0.98 0.96 0.93 0.87 0.85 0.82 0.80 0.73 0.76
Multiple R square of scores with factors 0.98 0.96 0.92 0.86 0.76 0.72 0.67 0.64 0.54 0.57
Minimum correlation of possible factor scores 0.96 0.92 0.83 0.72 0.52 0.44 0.34 0.27 0.08 0.15
MR10 MR12 MR13
Correlation of scores with factors 0.75 0.71 0.68
Multiple R square of scores with factors 0.56 0.50 0.47
Minimum correlation of possible factor scores 0.12 0.01 -0.07# count factors with eigenvalues > 1 and variance explained > 5%
efa_adult_max_unrot_nfactors <- efa_adult_max_unrot_eigenvalues %>%
filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
count() %>%
as.numeric()
efa_adult_max_unrot_nfactors[1] 4# manually check that each factor is the dominant factor for at least one mental capacity item
efa_adult_max_unrot_loadings <- fa.sort(loadings(efa_adult_max_unrot)[]) %>%
data.frame() %>%
select(1:efa_adult_max_unrot_nfactors) %>%
rename(F1 = MR1, F2 = MR2, F3 = MR3, F4 = MR4) %>% # adjust by hand as needed
mutate(F1_abs = abs(F1),
F2_abs = abs(F2),
F3_abs = abs(F3),
F4_abs = abs(F4),
loading_abs = pmax(F1_abs, F2_abs, F3_abs, F4_abs),
loading = ifelse(loading_abs == abs(F1), F1,
ifelse(loading_abs == abs(F2), F2,
ifelse(loading_abs == abs(F3), F3,
ifelse(loading_abs == abs(F4), F4,
NA)))),
factor = ifelse(loading == F1, "F1",
ifelse(loading == F2, "F2",
ifelse(loading == F3, "F3",
ifelse(loading == F4, "F4",
NA)))),
factorName = ifelse(loading == F1, "Factor 1",
ifelse(loading == F2, "Factor 2",
ifelse(loading == F3, "Factor 3",
ifelse(loading == F4, "Factor 4",
NA)))))
efa_adult_max_unrot_loadings %>% count(factorName) # drop any factors where n < 1# reset as needed
efa_adult_max_unrot_nfactors <- efa_adult_max_unrot_loadings %>% count(factorName) %>% nrow()Maximal (13-factor) rotated solution
# do factor analysis
efa_adult_max_rot <- fa(d3_adult, nfactors = 13, rotate = rot_type, cor = cor_type)
efa_adult_max_rotFactor Analysis using method = minres
Call: fa(r = d3_adult, nfactors = 13, rotate = rot_type, cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR8 MR1 MR7 MR3 MR2 MR5 MR4 MR10 MR13 MR9 MR11 MR6 MR12 h2
angry 0.40 0.48 0.06 0.04 0.03 0.23 0.19 0.14 -0.04 -0.19 -0.12 -0.05 -0.11 0.57
beliefs 0.06 0.25 0.73 0.06 0.11 0.08 0.10 0.06 0.10 -0.06 0.09 -0.02 -0.12 0.68
calm 0.56 0.39 0.16 0.05 0.02 0.11 0.05 0.07 0.12 -0.09 -0.02 -0.01 0.14 0.56
choices 0.07 0.10 0.13 0.23 0.16 0.66 -0.03 -0.02 -0.07 0.14 0.04 -0.11 -0.05 0.59
communicating -0.11 0.02 0.09 0.18 0.76 0.15 0.06 0.00 0.05 -0.04 -0.03 0.03 -0.02 0.67
computations -0.71 -0.06 0.23 0.14 0.47 0.05 0.02 0.07 -0.11 -0.01 -0.12 -0.06 -0.01 0.84
conscious 0.27 0.03 0.18 0.34 0.00 0.44 0.01 -0.13 0.07 0.03 -0.07 0.03 -0.04 0.45
depressed 0.23 0.84 0.14 0.03 0.01 0.07 0.05 0.05 0.11 0.07 -0.08 0.01 -0.05 0.81
depth 0.05 -0.01 0.09 0.61 0.13 0.26 0.02 -0.11 -0.06 0.02 0.06 0.00 -0.04 0.48
desires 0.53 0.31 0.12 0.12 -0.02 0.16 0.06 0.10 0.01 -0.04 0.43 -0.02 0.00 0.63
disrespected 0.17 0.68 0.13 0.08 0.01 0.03 0.26 -0.08 0.08 0.02 0.05 0.00 -0.05 0.60
embarrassed 0.11 0.42 0.15 -0.02 0.06 0.02 0.77 0.11 0.01 0.06 -0.03 0.02 0.12 0.84
emo_recog -0.05 0.25 0.54 0.10 0.10 0.14 0.05 -0.02 0.10 0.13 -0.07 0.24 0.02 0.49
fear 0.81 0.31 0.04 0.11 -0.01 0.04 -0.02 0.16 -0.10 -0.02 -0.13 0.20 -0.21 0.91
free_will 0.07 0.03 0.09 0.09 0.11 0.66 0.04 0.11 0.09 -0.09 0.05 0.03 0.02 0.51
goal 0.20 0.07 0.31 0.19 0.09 0.28 0.07 0.23 -0.02 0.00 0.12 0.02 -0.08 0.35
guilt 0.13 0.56 0.19 0.05 0.04 0.05 0.71 -0.09 0.02 -0.06 0.07 -0.03 -0.17 0.93
happy 0.34 0.79 0.17 -0.09 0.07 0.13 -0.03 -0.04 0.08 -0.01 -0.03 0.00 -0.03 0.81
hungry 0.85 0.08 -0.10 0.08 -0.35 0.02 0.06 -0.07 0.08 0.04 0.08 -0.01 0.06 0.89
intentions -0.21 0.11 0.29 0.09 0.35 0.26 0.07 0.44 0.19 -0.03 0.04 0.00 0.01 0.59
joy 0.33 0.78 0.24 -0.02 0.08 -0.01 0.09 0.00 -0.11 0.06 0.07 0.06 0.17 0.84
love 0.25 0.69 0.30 0.11 0.02 0.08 0.12 0.02 0.02 0.18 0.20 -0.01 -0.01 0.74
morality -0.12 0.25 0.45 0.13 0.23 0.08 0.05 0.23 0.02 0.12 0.00 -0.09 -0.04 0.43
nauseated 0.66 0.30 0.01 0.14 0.00 0.09 -0.02 -0.02 -0.09 0.07 0.15 -0.01 0.11 0.61
odors 0.60 0.01 0.02 0.31 -0.18 0.14 0.11 -0.10 0.09 0.05 -0.05 0.07 0.24 0.61
pain 0.85 0.17 -0.05 0.06 -0.05 0.02 0.08 -0.06 0.09 -0.03 -0.05 -0.14 -0.05 0.81
personality 0.04 0.31 0.56 -0.01 0.15 0.16 0.07 -0.07 -0.06 -0.05 0.05 0.02 0.14 0.50
pleasure 0.57 0.43 0.14 0.10 -0.02 0.00 0.08 -0.03 -0.12 0.44 -0.07 -0.01 0.02 0.76
pride 0.14 0.72 0.26 0.03 0.04 0.06 0.21 0.21 -0.03 -0.11 0.03 0.01 0.04 0.72
reasoning 0.06 0.09 0.14 0.20 0.36 0.52 0.03 -0.02 0.06 -0.07 -0.02 0.28 0.07 0.56
recognizing -0.25 0.06 0.26 0.21 0.73 0.15 0.00 0.03 -0.04 -0.04 -0.01 -0.07 0.05 0.75
remembering -0.23 0.11 0.12 0.25 0.56 0.23 0.00 0.11 0.12 0.21 0.12 0.04 -0.05 0.60
safe 0.70 0.24 0.18 0.15 -0.11 0.20 -0.01 0.00 0.13 -0.04 0.02 0.19 -0.11 0.72
seeing 0.18 0.01 0.00 0.63 0.11 0.19 0.05 0.02 0.09 0.07 -0.04 -0.05 0.00 0.50
self_aware 0.25 0.13 0.21 0.12 0.13 0.32 0.03 0.07 0.45 -0.04 0.02 0.04 0.00 0.47
self_restraint 0.04 0.28 0.44 0.09 0.09 0.26 0.05 0.02 0.05 0.00 -0.13 -0.16 0.07 0.42
sounds 0.12 0.06 -0.01 0.62 0.21 0.05 -0.03 0.11 -0.05 -0.04 0.04 0.12 0.02 0.48
temperature 0.13 0.02 0.13 0.73 0.07 0.02 -0.01 0.06 0.07 -0.02 0.00 -0.04 0.02 0.58
thoughts 0.28 0.25 0.25 -0.01 0.11 0.44 0.01 0.16 0.28 0.12 -0.10 -0.17 0.08 0.57
tired 0.74 0.23 0.05 0.21 -0.01 0.10 0.06 0.00 -0.03 0.01 0.01 -0.15 -0.04 0.69
u2 com
angry 0.427 3.9
beliefs 0.323 1.5
calm 0.445 2.5
choices 0.413 1.8
communicating 0.333 1.3
computations 0.155 2.3
conscious 0.545 3.5
depressed 0.191 1.3
depth 0.515 1.7
desires 0.370 3.3
disrespected 0.403 1.7
embarrassed 0.160 1.8
emo_recog 0.513 2.6
fear 0.087 1.8
free_will 0.492 1.3
goal 0.654 5.5
guilt 0.070 2.4
happy 0.189 1.6
hungry 0.112 1.5
intentions 0.415 4.9
joy 0.162 1.8
love 0.262 2.3
morality 0.570 3.7
nauseated 0.391 1.8
odors 0.391 2.5
pain 0.192 1.2
personality 0.501 2.3
pleasure 0.242 3.3
pride 0.282 1.9
reasoning 0.438 3.2
recognizing 0.251 1.9
remembering 0.396 3.3
safe 0.283 2.1
seeing 0.498 1.5
self_aware 0.529 3.7
self_restraint 0.582 3.5
sounds 0.519 1.6
temperature 0.423 1.2
thoughts 0.433 5.5
tired 0.313 1.5
MR8 MR1 MR7 MR3 MR2 MR5 MR4 MR10 MR13 MR9 MR11 MR6 MR12
SS loadings 6.47 5.37 2.53 2.41 2.36 2.33 1.36 0.57 0.55 0.46 0.42 0.38 0.32
Proportion Var 0.16 0.13 0.06 0.06 0.06 0.06 0.03 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.16 0.30 0.36 0.42 0.48 0.54 0.57 0.59 0.60 0.61 0.62 0.63 0.64
Proportion Explained 0.25 0.21 0.10 0.09 0.09 0.09 0.05 0.02 0.02 0.02 0.02 0.01 0.01
Cumulative Proportion 0.25 0.46 0.56 0.66 0.75 0.84 0.89 0.92 0.94 0.96 0.97 0.99 1.00
Mean item complexity = 2.5
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 27.45 with Chi Square of 5073.12
The degrees of freedom for the model are 338 and the objective function was 2.29
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 196 with the empirical chi square 118.47 with prob < 1
The total number of observations was 200 with MLE Chi Square = 403.84 with prob < 0.008
Tucker Lewis Index of factoring reliability = 0.963
RMSEA index = 0.042 and the 90 % confidence intervals are 0.017 0.042
BIC = -1386.99
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR8 MR1 MR7 MR3 MR2 MR5 MR4 MR10 MR13 MR9
Correlation of scores with factors 0.96 0.94 0.86 0.88 0.89 0.86 0.94 0.76 0.75 0.76
Multiple R square of scores with factors 0.93 0.89 0.73 0.77 0.78 0.73 0.87 0.58 0.56 0.58
Minimum correlation of possible factor scores 0.86 0.78 0.47 0.55 0.57 0.47 0.75 0.17 0.12 0.16
MR11 MR6 MR12
Correlation of scores with factors 0.75 0.74 0.77
Multiple R square of scores with factors 0.57 0.54 0.60
Minimum correlation of possible factor scores 0.14 0.09 0.20# examine eigenvalues and variance explained
efa_adult_max_rot_eigenvalues <- print(efa_adult_max_rot)$Vaccounted %>%
t() %>%
data.frame()Factor Analysis using method = minres
Call: fa(r = d3_adult, nfactors = 13, rotate = rot_type, cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR8 MR1 MR7 MR3 MR2 MR5 MR4 MR10 MR13 MR9 MR11 MR6 MR12 h2
angry 0.40 0.48 0.06 0.04 0.03 0.23 0.19 0.14 -0.04 -0.19 -0.12 -0.05 -0.11 0.57
beliefs 0.06 0.25 0.73 0.06 0.11 0.08 0.10 0.06 0.10 -0.06 0.09 -0.02 -0.12 0.68
calm 0.56 0.39 0.16 0.05 0.02 0.11 0.05 0.07 0.12 -0.09 -0.02 -0.01 0.14 0.56
choices 0.07 0.10 0.13 0.23 0.16 0.66 -0.03 -0.02 -0.07 0.14 0.04 -0.11 -0.05 0.59
communicating -0.11 0.02 0.09 0.18 0.76 0.15 0.06 0.00 0.05 -0.04 -0.03 0.03 -0.02 0.67
computations -0.71 -0.06 0.23 0.14 0.47 0.05 0.02 0.07 -0.11 -0.01 -0.12 -0.06 -0.01 0.84
conscious 0.27 0.03 0.18 0.34 0.00 0.44 0.01 -0.13 0.07 0.03 -0.07 0.03 -0.04 0.45
depressed 0.23 0.84 0.14 0.03 0.01 0.07 0.05 0.05 0.11 0.07 -0.08 0.01 -0.05 0.81
depth 0.05 -0.01 0.09 0.61 0.13 0.26 0.02 -0.11 -0.06 0.02 0.06 0.00 -0.04 0.48
desires 0.53 0.31 0.12 0.12 -0.02 0.16 0.06 0.10 0.01 -0.04 0.43 -0.02 0.00 0.63
disrespected 0.17 0.68 0.13 0.08 0.01 0.03 0.26 -0.08 0.08 0.02 0.05 0.00 -0.05 0.60
embarrassed 0.11 0.42 0.15 -0.02 0.06 0.02 0.77 0.11 0.01 0.06 -0.03 0.02 0.12 0.84
emo_recog -0.05 0.25 0.54 0.10 0.10 0.14 0.05 -0.02 0.10 0.13 -0.07 0.24 0.02 0.49
fear 0.81 0.31 0.04 0.11 -0.01 0.04 -0.02 0.16 -0.10 -0.02 -0.13 0.20 -0.21 0.91
free_will 0.07 0.03 0.09 0.09 0.11 0.66 0.04 0.11 0.09 -0.09 0.05 0.03 0.02 0.51
goal 0.20 0.07 0.31 0.19 0.09 0.28 0.07 0.23 -0.02 0.00 0.12 0.02 -0.08 0.35
guilt 0.13 0.56 0.19 0.05 0.04 0.05 0.71 -0.09 0.02 -0.06 0.07 -0.03 -0.17 0.93
happy 0.34 0.79 0.17 -0.09 0.07 0.13 -0.03 -0.04 0.08 -0.01 -0.03 0.00 -0.03 0.81
hungry 0.85 0.08 -0.10 0.08 -0.35 0.02 0.06 -0.07 0.08 0.04 0.08 -0.01 0.06 0.89
intentions -0.21 0.11 0.29 0.09 0.35 0.26 0.07 0.44 0.19 -0.03 0.04 0.00 0.01 0.59
joy 0.33 0.78 0.24 -0.02 0.08 -0.01 0.09 0.00 -0.11 0.06 0.07 0.06 0.17 0.84
love 0.25 0.69 0.30 0.11 0.02 0.08 0.12 0.02 0.02 0.18 0.20 -0.01 -0.01 0.74
morality -0.12 0.25 0.45 0.13 0.23 0.08 0.05 0.23 0.02 0.12 0.00 -0.09 -0.04 0.43
nauseated 0.66 0.30 0.01 0.14 0.00 0.09 -0.02 -0.02 -0.09 0.07 0.15 -0.01 0.11 0.61
odors 0.60 0.01 0.02 0.31 -0.18 0.14 0.11 -0.10 0.09 0.05 -0.05 0.07 0.24 0.61
pain 0.85 0.17 -0.05 0.06 -0.05 0.02 0.08 -0.06 0.09 -0.03 -0.05 -0.14 -0.05 0.81
personality 0.04 0.31 0.56 -0.01 0.15 0.16 0.07 -0.07 -0.06 -0.05 0.05 0.02 0.14 0.50
pleasure 0.57 0.43 0.14 0.10 -0.02 0.00 0.08 -0.03 -0.12 0.44 -0.07 -0.01 0.02 0.76
pride 0.14 0.72 0.26 0.03 0.04 0.06 0.21 0.21 -0.03 -0.11 0.03 0.01 0.04 0.72
reasoning 0.06 0.09 0.14 0.20 0.36 0.52 0.03 -0.02 0.06 -0.07 -0.02 0.28 0.07 0.56
recognizing -0.25 0.06 0.26 0.21 0.73 0.15 0.00 0.03 -0.04 -0.04 -0.01 -0.07 0.05 0.75
remembering -0.23 0.11 0.12 0.25 0.56 0.23 0.00 0.11 0.12 0.21 0.12 0.04 -0.05 0.60
safe 0.70 0.24 0.18 0.15 -0.11 0.20 -0.01 0.00 0.13 -0.04 0.02 0.19 -0.11 0.72
seeing 0.18 0.01 0.00 0.63 0.11 0.19 0.05 0.02 0.09 0.07 -0.04 -0.05 0.00 0.50
self_aware 0.25 0.13 0.21 0.12 0.13 0.32 0.03 0.07 0.45 -0.04 0.02 0.04 0.00 0.47
self_restraint 0.04 0.28 0.44 0.09 0.09 0.26 0.05 0.02 0.05 0.00 -0.13 -0.16 0.07 0.42
sounds 0.12 0.06 -0.01 0.62 0.21 0.05 -0.03 0.11 -0.05 -0.04 0.04 0.12 0.02 0.48
temperature 0.13 0.02 0.13 0.73 0.07 0.02 -0.01 0.06 0.07 -0.02 0.00 -0.04 0.02 0.58
thoughts 0.28 0.25 0.25 -0.01 0.11 0.44 0.01 0.16 0.28 0.12 -0.10 -0.17 0.08 0.57
tired 0.74 0.23 0.05 0.21 -0.01 0.10 0.06 0.00 -0.03 0.01 0.01 -0.15 -0.04 0.69
u2 com
angry 0.427 3.9
beliefs 0.323 1.5
calm 0.445 2.5
choices 0.413 1.8
communicating 0.333 1.3
computations 0.155 2.3
conscious 0.545 3.5
depressed 0.191 1.3
depth 0.515 1.7
desires 0.370 3.3
disrespected 0.403 1.7
embarrassed 0.160 1.8
emo_recog 0.513 2.6
fear 0.087 1.8
free_will 0.492 1.3
goal 0.654 5.5
guilt 0.070 2.4
happy 0.189 1.6
hungry 0.112 1.5
intentions 0.415 4.9
joy 0.162 1.8
love 0.262 2.3
morality 0.570 3.7
nauseated 0.391 1.8
odors 0.391 2.5
pain 0.192 1.2
personality 0.501 2.3
pleasure 0.242 3.3
pride 0.282 1.9
reasoning 0.438 3.2
recognizing 0.251 1.9
remembering 0.396 3.3
safe 0.283 2.1
seeing 0.498 1.5
self_aware 0.529 3.7
self_restraint 0.582 3.5
sounds 0.519 1.6
temperature 0.423 1.2
thoughts 0.433 5.5
tired 0.313 1.5
MR8 MR1 MR7 MR3 MR2 MR5 MR4 MR10 MR13 MR9 MR11 MR6 MR12
SS loadings 6.47 5.37 2.53 2.41 2.36 2.33 1.36 0.57 0.55 0.46 0.42 0.38 0.32
Proportion Var 0.16 0.13 0.06 0.06 0.06 0.06 0.03 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.16 0.30 0.36 0.42 0.48 0.54 0.57 0.59 0.60 0.61 0.62 0.63 0.64
Proportion Explained 0.25 0.21 0.10 0.09 0.09 0.09 0.05 0.02 0.02 0.02 0.02 0.01 0.01
Cumulative Proportion 0.25 0.46 0.56 0.66 0.75 0.84 0.89 0.92 0.94 0.96 0.97 0.99 1.00
Mean item complexity = 2.5
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 27.45 with Chi Square of 5073.12
The degrees of freedom for the model are 338 and the objective function was 2.29
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 196 with the empirical chi square 118.47 with prob < 1
The total number of observations was 200 with MLE Chi Square = 403.84 with prob < 0.008
Tucker Lewis Index of factoring reliability = 0.963
RMSEA index = 0.042 and the 90 % confidence intervals are 0.017 0.042
BIC = -1386.99
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR8 MR1 MR7 MR3 MR2 MR5 MR4 MR10 MR13 MR9
Correlation of scores with factors 0.96 0.94 0.86 0.88 0.89 0.86 0.94 0.76 0.75 0.76
Multiple R square of scores with factors 0.93 0.89 0.73 0.77 0.78 0.73 0.87 0.58 0.56 0.58
Minimum correlation of possible factor scores 0.86 0.78 0.47 0.55 0.57 0.47 0.75 0.17 0.12 0.16
MR11 MR6 MR12
Correlation of scores with factors 0.75 0.74 0.77
Multiple R square of scores with factors 0.57 0.54 0.60
Minimum correlation of possible factor scores 0.14 0.09 0.20efa_adult_max_rot_eigenvalues# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"Small rotated solution
# do factor analysis
efa_adult_small_rot <- fa(d3_adult, nfactors = efa_adult_max_unrot_nfactors, rotate = rot_type, cor = cor_type)
efa_adult_small_rotFactor Analysis using method = minres
Call: fa(r = d3_adult, nfactors = efa_adult_max_unrot_nfactors, rotate = rot_type,
cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR2 MR1 MR3 h2 u2 com
angry 0.51 0.38 0.15 0.43 0.57 2.0
beliefs 0.51 -0.03 0.33 0.37 0.63 1.7
calm 0.43 0.53 0.17 0.50 0.50 2.1
choices 0.14 0.08 0.57 0.35 0.65 1.2
communicating 0.14 -0.32 0.57 0.44 0.56 1.7
computations 0.05 -0.83 0.36 0.82 0.18 1.4
conscious 0.06 0.32 0.50 0.36 0.64 1.7
depressed 0.80 0.26 0.04 0.71 0.29 1.2
depth -0.03 0.10 0.57 0.34 0.66 1.1
desires 0.36 0.53 0.19 0.45 0.55 2.0
disrespected 0.70 0.21 0.03 0.54 0.46 1.2
embarrassed 0.60 0.09 0.03 0.37 0.63 1.1
emo_recog 0.42 -0.09 0.31 0.28 0.72 1.9
fear 0.32 0.75 0.13 0.68 0.32 1.4
free_will 0.09 0.09 0.48 0.24 0.76 1.1
goal 0.21 0.16 0.42 0.25 0.75 1.8
guilt 0.69 0.14 0.06 0.50 0.50 1.1
happy 0.77 0.33 0.05 0.71 0.29 1.4
hungry 0.04 0.93 -0.08 0.88 0.12 1.0
intentions 0.28 -0.31 0.46 0.39 0.61 2.5
joy 0.81 0.30 0.02 0.75 0.25 1.3
love 0.76 0.27 0.14 0.66 0.34 1.3
morality 0.42 -0.20 0.34 0.34 0.66 2.4
nauseated 0.29 0.66 0.14 0.54 0.46 1.5
odors 0.01 0.67 0.21 0.50 0.50 1.2
pain 0.17 0.82 0.06 0.71 0.29 1.1
personality 0.50 -0.05 0.26 0.32 0.68 1.5
pleasure 0.47 0.55 0.08 0.53 0.47 2.0
pride 0.81 0.13 0.08 0.67 0.33 1.1
reasoning 0.16 0.00 0.59 0.38 0.62 1.1
recognizing 0.21 -0.45 0.61 0.61 0.39 2.1
remembering 0.19 -0.33 0.58 0.48 0.52 1.9
safe 0.28 0.71 0.23 0.64 0.36 1.5
seeing -0.03 0.24 0.55 0.36 0.64 1.4
self_aware 0.21 0.23 0.41 0.27 0.73 2.2
self_restraint 0.42 0.00 0.34 0.29 0.71 1.9
sounds 0.01 0.13 0.50 0.27 0.73 1.1
temperature 0.00 0.19 0.51 0.30 0.70 1.3
thoughts 0.35 0.24 0.37 0.32 0.68 2.7
tired 0.24 0.73 0.22 0.63 0.37 1.4
MR2 MR1 MR3
SS loadings 7.11 7.09 4.97
Proportion Var 0.18 0.18 0.12
Cumulative Var 0.18 0.35 0.48
Proportion Explained 0.37 0.37 0.26
Cumulative Proportion 0.37 0.74 1.00
Mean item complexity = 1.6
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 27.45 with Chi Square of 5073.12
The degrees of freedom for the model are 663 and the objective function was 6.63
The root mean square of the residuals (RMSR) is 0.05
The df corrected root mean square of the residuals is 0.05
The harmonic number of observations is 196 with the empirical chi square 754.59 with prob < 0.0076
The total number of observations was 200 with MLE Chi Square = 1211.29 with prob < 1.3e-34
Tucker Lewis Index of factoring reliability = 0.848
RMSEA index = 0.07 and the 90 % confidence intervals are 0.059 NA
BIC = -2301.5
Fit based upon off diagonal values = 0.97
Measures of factor score adequacy
MR2 MR1 MR3
Correlation of scores with factors 0.96 0.98 0.94
Multiple R square of scores with factors 0.93 0.95 0.88
Minimum correlation of possible factor scores 0.86 0.91 0.76# examine eigenvalues and variance explained
efa_adult_small_rot_eigenvalues <- print(efa_adult_small_rot)$Vaccounted %>%
t() %>%
data.frame()Factor Analysis using method = minres
Call: fa(r = d3_adult, nfactors = efa_adult_max_unrot_nfactors, rotate = rot_type,
cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR2 MR1 MR3 h2 u2 com
angry 0.51 0.38 0.15 0.43 0.57 2.0
beliefs 0.51 -0.03 0.33 0.37 0.63 1.7
calm 0.43 0.53 0.17 0.50 0.50 2.1
choices 0.14 0.08 0.57 0.35 0.65 1.2
communicating 0.14 -0.32 0.57 0.44 0.56 1.7
computations 0.05 -0.83 0.36 0.82 0.18 1.4
conscious 0.06 0.32 0.50 0.36 0.64 1.7
depressed 0.80 0.26 0.04 0.71 0.29 1.2
depth -0.03 0.10 0.57 0.34 0.66 1.1
desires 0.36 0.53 0.19 0.45 0.55 2.0
disrespected 0.70 0.21 0.03 0.54 0.46 1.2
embarrassed 0.60 0.09 0.03 0.37 0.63 1.1
emo_recog 0.42 -0.09 0.31 0.28 0.72 1.9
fear 0.32 0.75 0.13 0.68 0.32 1.4
free_will 0.09 0.09 0.48 0.24 0.76 1.1
goal 0.21 0.16 0.42 0.25 0.75 1.8
guilt 0.69 0.14 0.06 0.50 0.50 1.1
happy 0.77 0.33 0.05 0.71 0.29 1.4
hungry 0.04 0.93 -0.08 0.88 0.12 1.0
intentions 0.28 -0.31 0.46 0.39 0.61 2.5
joy 0.81 0.30 0.02 0.75 0.25 1.3
love 0.76 0.27 0.14 0.66 0.34 1.3
morality 0.42 -0.20 0.34 0.34 0.66 2.4
nauseated 0.29 0.66 0.14 0.54 0.46 1.5
odors 0.01 0.67 0.21 0.50 0.50 1.2
pain 0.17 0.82 0.06 0.71 0.29 1.1
personality 0.50 -0.05 0.26 0.32 0.68 1.5
pleasure 0.47 0.55 0.08 0.53 0.47 2.0
pride 0.81 0.13 0.08 0.67 0.33 1.1
reasoning 0.16 0.00 0.59 0.38 0.62 1.1
recognizing 0.21 -0.45 0.61 0.61 0.39 2.1
remembering 0.19 -0.33 0.58 0.48 0.52 1.9
safe 0.28 0.71 0.23 0.64 0.36 1.5
seeing -0.03 0.24 0.55 0.36 0.64 1.4
self_aware 0.21 0.23 0.41 0.27 0.73 2.2
self_restraint 0.42 0.00 0.34 0.29 0.71 1.9
sounds 0.01 0.13 0.50 0.27 0.73 1.1
temperature 0.00 0.19 0.51 0.30 0.70 1.3
thoughts 0.35 0.24 0.37 0.32 0.68 2.7
tired 0.24 0.73 0.22 0.63 0.37 1.4
MR2 MR1 MR3
SS loadings 7.11 7.09 4.97
Proportion Var 0.18 0.18 0.12
Cumulative Var 0.18 0.35 0.48
Proportion Explained 0.37 0.37 0.26
Cumulative Proportion 0.37 0.74 1.00
Mean item complexity = 1.6
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 27.45 with Chi Square of 5073.12
The degrees of freedom for the model are 663 and the objective function was 6.63
The root mean square of the residuals (RMSR) is 0.05
The df corrected root mean square of the residuals is 0.05
The harmonic number of observations is 196 with the empirical chi square 754.59 with prob < 0.0076
The total number of observations was 200 with MLE Chi Square = 1211.29 with prob < 1.3e-34
Tucker Lewis Index of factoring reliability = 0.848
RMSEA index = 0.07 and the 90 % confidence intervals are 0.059 NA
BIC = -2301.5
Fit based upon off diagonal values = 0.97
Measures of factor score adequacy
MR2 MR1 MR3
Correlation of scores with factors 0.96 0.98 0.94
Multiple R square of scores with factors 0.93 0.95 0.88
Minimum correlation of possible factor scores 0.86 0.91 0.76efa_adult_small_rot_eigenvalues# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"Loadings
# make dataframe for all factor loadings and dominant factor
efa_adult_small_rot_loadings <- efa_adult_small_rot$loadings[] %>%
fa.sort() %>%
data.frame() %>%
rownames_to_column(var = "capacity") %>%
rename(F1 = MR1, F2 = MR2, F3 = MR3) %>% # adjust by hand as needed
mutate(F1_abs = abs(F1),
F2_abs = abs(F2),
F3_abs = abs(F3),
loading_abs = pmax(F1_abs, F2_abs, F3_abs),
loading = ifelse(loading_abs == abs(F1), F1,
ifelse(loading_abs == abs(F2), F2,
ifelse(loading_abs == abs(F3), F3,
NA))),
factor = ifelse(loading == F1, "F1",
ifelse(loading == F2, "F2",
ifelse(loading == F3, "F3",
NA))),
factorName = ifelse(loading == F1, "Factor 1",
ifelse(loading == F2, "Factor 2",
ifelse(loading == F3, "Factor 3",
NA)))) %>%
select(capacity, F1, F2, F3, factor, factorName, loading, loading_abs) %>%
distinct() %>%
full_join(d %>% select(capacity, capWording)) %>%
mutate(capWording = gsub("\\ ", "_", gsub(" --.*", "", capWording))) %>%
select(capacity, capWording, F1:loading_abs) %>%
distinct()Joining, by = "capacity"
joining factor and character vector, coercing into character vector# print out list of items by dominant factor
efa_adult_small_rot_items <- efa_adult_small_rot_loadings %>%
arrange(factor, desc(loading_abs)) %>%
mutate(capWordingPlus = ifelse(loading > 0, capWording,
paste0(capWording, " (negative loading)"))) %>%
group_by(factor) %>%
summarise(items = gsub("_", " ", paste(capWordingPlus, collapse = ", ")))
head(efa_adult_small_rot_items$items, 40)[1] "get hungry, do math (negative loading), feel pain, feel scared, feel tired, feel safe, smell things, feel sick, feel pleasure, feel calm, have desires"
[2] "feel joy, feel proud, feel sad, feel happy, feel love, get hurt feelings, feel guilty, feel embarrassed, get angry, have beliefs, have a personality, understand how somebody else is feeling, know what's nice and what's mean, have self-control"
[3] "recognize somebody else, figure out how to do things, remember things, sense whether something is close by or far away, communicate with somebody else, make choices, see things, sense temperatures, hear sounds, be aware of things, decide what to do, make plans, have goals, be aware of itself, have thoughts"Children alone
Exploratory factor analysis
Maximal (13-factor) unrotated solution
# do factor analysis
efa_child_max_unrot <- fa(d3_child, nfactors = 13, rotate = "none", cor = cor_type)
efa_child_max_unrotFactor Analysis using method = minres
Call: fa(r = d3_child, nfactors = 13, rotate = "none", cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR5 MR6 MR4 MR7 MR8 MR9 MR11 MR10 MR12 MR13 h2
angry 0.59 -0.05 -0.09 0.14 -0.04 0.05 0.15 0.06 -0.02 -0.04 -0.06 -0.06 -0.02 0.41
beliefs 0.47 0.32 -0.05 0.14 -0.03 0.08 0.11 -0.15 -0.04 0.16 0.03 -0.01 0.10 0.43
calm 0.55 0.04 -0.01 -0.04 0.04 -0.07 -0.08 -0.09 -0.02 -0.15 0.10 0.15 0.10 0.39
choices 0.41 0.09 0.23 0.44 0.06 0.05 -0.05 -0.20 -0.09 0.05 -0.14 -0.19 0.02 0.53
communicating 0.03 0.27 0.15 -0.14 0.32 0.00 0.05 0.05 0.30 -0.05 -0.01 -0.12 -0.09 0.34
computations -0.16 0.79 0.08 -0.01 -0.04 0.08 0.09 -0.17 0.01 -0.12 0.05 -0.10 0.05 0.74
conscious 0.35 0.09 0.46 -0.07 0.31 -0.02 0.11 0.26 -0.13 0.00 0.14 0.07 0.05 0.57
depressed 0.70 0.04 -0.16 -0.05 0.08 0.05 -0.13 0.08 0.12 -0.21 -0.16 -0.08 0.05 0.64
depth 0.11 0.19 0.41 -0.11 -0.13 0.25 0.11 -0.11 0.06 0.08 0.12 0.20 -0.07 0.40
desires 0.53 -0.11 -0.10 0.09 0.15 0.00 0.11 -0.13 0.00 0.13 -0.02 0.15 0.14 0.42
disrespected 0.67 0.13 -0.14 0.11 0.06 0.12 -0.08 0.13 -0.12 0.07 0.14 -0.06 -0.03 0.59
embarrassed 0.50 0.16 -0.31 0.02 0.01 0.26 0.40 0.09 -0.08 -0.01 -0.07 -0.02 0.01 0.62
emo_recog 0.25 0.54 0.03 -0.05 -0.12 0.06 0.03 0.26 0.07 0.02 0.06 -0.10 0.02 0.46
fear 0.62 -0.29 0.04 -0.05 0.09 -0.10 0.10 0.07 0.24 -0.05 -0.21 0.22 -0.09 0.67
free_will 0.49 0.04 0.29 0.27 0.12 0.01 -0.08 -0.26 -0.06 0.11 -0.07 0.09 -0.06 0.51
goal 0.28 0.37 0.00 0.12 0.04 -0.07 0.04 -0.03 0.11 -0.16 -0.10 0.09 0.25 0.36
guilt 0.54 0.21 -0.16 -0.10 -0.06 0.22 0.27 -0.09 -0.08 0.06 0.08 0.16 -0.10 0.55
happy 0.69 0.25 -0.21 -0.16 0.21 -0.04 -0.07 -0.07 -0.02 -0.01 0.06 -0.06 -0.14 0.69
hungry 0.51 -0.70 0.12 -0.04 -0.06 0.15 -0.04 -0.02 -0.03 0.01 -0.04 -0.02 -0.02 0.80
intentions 0.27 0.44 0.20 0.25 -0.04 0.16 -0.06 -0.09 -0.11 -0.15 -0.03 0.06 -0.28 0.53
joy 0.68 0.17 -0.27 -0.10 0.15 -0.03 -0.13 -0.07 0.02 0.02 0.13 -0.11 0.01 0.65
love 0.58 0.16 -0.20 -0.14 0.02 0.16 -0.12 0.11 -0.13 0.12 0.05 0.05 0.04 0.51
morality 0.38 0.44 -0.04 0.00 -0.46 -0.10 -0.09 0.14 0.18 0.19 0.11 0.02 -0.06 0.68
nauseated 0.37 -0.38 0.02 -0.11 -0.15 0.23 0.09 -0.03 0.11 -0.01 -0.15 -0.07 0.05 0.41
odors 0.26 -0.55 0.29 0.04 -0.01 0.05 0.19 -0.14 0.22 -0.04 0.27 -0.13 -0.10 0.67
pain 0.56 -0.57 0.12 0.05 -0.07 0.09 -0.12 -0.02 0.00 0.11 0.05 -0.03 0.12 0.71
personality 0.48 0.37 0.03 0.17 -0.20 0.10 0.16 0.03 0.11 -0.02 -0.06 -0.05 0.19 0.52
pleasure 0.59 0.17 -0.17 -0.16 0.08 -0.22 -0.16 -0.21 0.06 -0.01 0.10 0.14 0.07 0.59
pride 0.63 0.31 -0.28 -0.08 0.01 -0.04 0.01 -0.01 -0.07 -0.03 -0.05 -0.01 -0.09 0.60
reasoning 0.18 0.27 0.34 0.06 0.21 0.00 -0.08 0.03 0.20 0.13 -0.01 0.09 0.18 0.37
recognizing 0.14 0.33 0.17 -0.06 -0.05 0.05 0.09 -0.06 0.29 -0.05 0.10 0.11 0.11 0.29
remembering -0.03 0.55 0.24 -0.05 0.16 0.07 -0.07 0.01 0.14 0.29 -0.31 -0.11 -0.09 0.61
safe 0.64 0.00 0.32 -0.12 -0.19 -0.44 0.12 -0.03 -0.12 -0.03 -0.08 -0.07 -0.04 0.80
seeing -0.09 0.08 0.21 0.11 0.16 -0.01 -0.14 0.16 0.13 0.19 0.13 0.02 -0.16 0.24
self_aware 0.24 0.19 0.46 0.17 0.15 -0.07 0.12 0.33 -0.18 -0.03 0.05 0.06 0.04 0.52
self_restraint 0.29 0.21 0.11 0.37 -0.17 0.13 -0.33 0.10 0.05 -0.25 0.02 0.16 -0.09 0.54
sounds -0.08 0.03 0.33 0.09 0.19 -0.05 0.16 -0.05 0.17 -0.14 0.17 -0.13 -0.04 0.28
temperature -0.09 0.27 0.54 -0.43 -0.08 0.29 -0.15 -0.09 -0.12 -0.06 -0.06 0.02 0.04 0.71
thoughts 0.57 0.10 0.18 0.05 -0.01 -0.11 -0.16 0.04 -0.03 0.00 0.09 -0.13 0.15 0.46
tired 0.45 -0.28 0.02 -0.11 -0.05 0.23 -0.10 0.21 0.09 -0.16 -0.04 -0.15 -0.01 0.45
u2 com
angry 0.59 1.4
beliefs 0.57 3.0
calm 0.61 1.7
choices 0.47 4.0
communicating 0.66 4.6
computations 0.26 1.4
conscious 0.43 4.3
depressed 0.36 1.7
depth 0.60 4.4
desires 0.58 2.2
disrespected 0.41 1.7
embarrassed 0.38 3.8
emo_recog 0.54 2.3
fear 0.33 2.7
free_will 0.49 3.6
goal 0.64 4.3
guilt 0.45 3.2
happy 0.31 2.0
hungry 0.20 2.1
intentions 0.47 4.9
joy 0.35 1.9
love 0.49 2.2
morality 0.32 4.3
nauseated 0.59 4.0
odors 0.33 3.8
pain 0.29 2.5
personality 0.48 3.7
pleasure 0.41 2.7
pride 0.40 2.0
reasoning 0.63 5.6
recognizing 0.71 4.2
remembering 0.39 3.4
safe 0.20 3.0
seeing 0.76 8.8
self_aware 0.48 4.4
self_restraint 0.46 6.4
sounds 0.72 5.0
temperature 0.29 3.7
thoughts 0.54 1.9
tired 0.55 4.0
MR1 MR2 MR3 MR5 MR6 MR4 MR7 MR8 MR9 MR11 MR10 MR12 MR13
SS loadings 8.10 4.24 2.16 1.00 0.89 0.82 0.78 0.70 0.64 0.53 0.52 0.46 0.44
Proportion Var 0.20 0.11 0.05 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01
Cumulative Var 0.20 0.31 0.36 0.39 0.41 0.43 0.45 0.47 0.48 0.50 0.51 0.52 0.53
Proportion Explained 0.38 0.20 0.10 0.05 0.04 0.04 0.04 0.03 0.03 0.02 0.02 0.02 0.02
Cumulative Proportion 0.38 0.58 0.68 0.73 0.77 0.81 0.85 0.88 0.91 0.93 0.96 0.98 1.00
Mean item complexity = 3.4
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 17.09 with Chi Square of 3159.59
The degrees of freedom for the model are 338 and the objective function was 1.74
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 198 with the empirical chi square 157.13 with prob < 1
The total number of observations was 200 with MLE Chi Square = 306.92 with prob < 0.89
Tucker Lewis Index of factoring reliability = 1.032
RMSEA index = 0.011 and the 90 % confidence intervals are NA 0.013
BIC = -1483.91
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR3 MR5 MR6 MR4 MR7 MR8 MR9 MR11
Correlation of scores with factors 0.98 0.96 0.92 0.84 0.83 0.84 0.81 0.78 0.76 0.73
Multiple R square of scores with factors 0.95 0.93 0.84 0.70 0.68 0.71 0.65 0.61 0.58 0.54
Minimum correlation of possible factor scores 0.91 0.85 0.69 0.41 0.37 0.42 0.30 0.22 0.17 0.08
MR10 MR12 MR13
Correlation of scores with factors 0.75 0.71 0.69
Multiple R square of scores with factors 0.56 0.50 0.48
Minimum correlation of possible factor scores 0.12 0.00 -0.05# examine eigenvalues and variance explained
efa_child_max_unrot_eigenvalues <- print(efa_child_max_unrot)$Vaccounted %>%
t() %>%
data.frame()Factor Analysis using method = minres
Call: fa(r = d3_child, nfactors = 13, rotate = "none", cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR5 MR6 MR4 MR7 MR8 MR9 MR11 MR10 MR12 MR13 h2
angry 0.59 -0.05 -0.09 0.14 -0.04 0.05 0.15 0.06 -0.02 -0.04 -0.06 -0.06 -0.02 0.41
beliefs 0.47 0.32 -0.05 0.14 -0.03 0.08 0.11 -0.15 -0.04 0.16 0.03 -0.01 0.10 0.43
calm 0.55 0.04 -0.01 -0.04 0.04 -0.07 -0.08 -0.09 -0.02 -0.15 0.10 0.15 0.10 0.39
choices 0.41 0.09 0.23 0.44 0.06 0.05 -0.05 -0.20 -0.09 0.05 -0.14 -0.19 0.02 0.53
communicating 0.03 0.27 0.15 -0.14 0.32 0.00 0.05 0.05 0.30 -0.05 -0.01 -0.12 -0.09 0.34
computations -0.16 0.79 0.08 -0.01 -0.04 0.08 0.09 -0.17 0.01 -0.12 0.05 -0.10 0.05 0.74
conscious 0.35 0.09 0.46 -0.07 0.31 -0.02 0.11 0.26 -0.13 0.00 0.14 0.07 0.05 0.57
depressed 0.70 0.04 -0.16 -0.05 0.08 0.05 -0.13 0.08 0.12 -0.21 -0.16 -0.08 0.05 0.64
depth 0.11 0.19 0.41 -0.11 -0.13 0.25 0.11 -0.11 0.06 0.08 0.12 0.20 -0.07 0.40
desires 0.53 -0.11 -0.10 0.09 0.15 0.00 0.11 -0.13 0.00 0.13 -0.02 0.15 0.14 0.42
disrespected 0.67 0.13 -0.14 0.11 0.06 0.12 -0.08 0.13 -0.12 0.07 0.14 -0.06 -0.03 0.59
embarrassed 0.50 0.16 -0.31 0.02 0.01 0.26 0.40 0.09 -0.08 -0.01 -0.07 -0.02 0.01 0.62
emo_recog 0.25 0.54 0.03 -0.05 -0.12 0.06 0.03 0.26 0.07 0.02 0.06 -0.10 0.02 0.46
fear 0.62 -0.29 0.04 -0.05 0.09 -0.10 0.10 0.07 0.24 -0.05 -0.21 0.22 -0.09 0.67
free_will 0.49 0.04 0.29 0.27 0.12 0.01 -0.08 -0.26 -0.06 0.11 -0.07 0.09 -0.06 0.51
goal 0.28 0.37 0.00 0.12 0.04 -0.07 0.04 -0.03 0.11 -0.16 -0.10 0.09 0.25 0.36
guilt 0.54 0.21 -0.16 -0.10 -0.06 0.22 0.27 -0.09 -0.08 0.06 0.08 0.16 -0.10 0.55
happy 0.69 0.25 -0.21 -0.16 0.21 -0.04 -0.07 -0.07 -0.02 -0.01 0.06 -0.06 -0.14 0.69
hungry 0.51 -0.70 0.12 -0.04 -0.06 0.15 -0.04 -0.02 -0.03 0.01 -0.04 -0.02 -0.02 0.80
intentions 0.27 0.44 0.20 0.25 -0.04 0.16 -0.06 -0.09 -0.11 -0.15 -0.03 0.06 -0.28 0.53
joy 0.68 0.17 -0.27 -0.10 0.15 -0.03 -0.13 -0.07 0.02 0.02 0.13 -0.11 0.01 0.65
love 0.58 0.16 -0.20 -0.14 0.02 0.16 -0.12 0.11 -0.13 0.12 0.05 0.05 0.04 0.51
morality 0.38 0.44 -0.04 0.00 -0.46 -0.10 -0.09 0.14 0.18 0.19 0.11 0.02 -0.06 0.68
nauseated 0.37 -0.38 0.02 -0.11 -0.15 0.23 0.09 -0.03 0.11 -0.01 -0.15 -0.07 0.05 0.41
odors 0.26 -0.55 0.29 0.04 -0.01 0.05 0.19 -0.14 0.22 -0.04 0.27 -0.13 -0.10 0.67
pain 0.56 -0.57 0.12 0.05 -0.07 0.09 -0.12 -0.02 0.00 0.11 0.05 -0.03 0.12 0.71
personality 0.48 0.37 0.03 0.17 -0.20 0.10 0.16 0.03 0.11 -0.02 -0.06 -0.05 0.19 0.52
pleasure 0.59 0.17 -0.17 -0.16 0.08 -0.22 -0.16 -0.21 0.06 -0.01 0.10 0.14 0.07 0.59
pride 0.63 0.31 -0.28 -0.08 0.01 -0.04 0.01 -0.01 -0.07 -0.03 -0.05 -0.01 -0.09 0.60
reasoning 0.18 0.27 0.34 0.06 0.21 0.00 -0.08 0.03 0.20 0.13 -0.01 0.09 0.18 0.37
recognizing 0.14 0.33 0.17 -0.06 -0.05 0.05 0.09 -0.06 0.29 -0.05 0.10 0.11 0.11 0.29
remembering -0.03 0.55 0.24 -0.05 0.16 0.07 -0.07 0.01 0.14 0.29 -0.31 -0.11 -0.09 0.61
safe 0.64 0.00 0.32 -0.12 -0.19 -0.44 0.12 -0.03 -0.12 -0.03 -0.08 -0.07 -0.04 0.80
seeing -0.09 0.08 0.21 0.11 0.16 -0.01 -0.14 0.16 0.13 0.19 0.13 0.02 -0.16 0.24
self_aware 0.24 0.19 0.46 0.17 0.15 -0.07 0.12 0.33 -0.18 -0.03 0.05 0.06 0.04 0.52
self_restraint 0.29 0.21 0.11 0.37 -0.17 0.13 -0.33 0.10 0.05 -0.25 0.02 0.16 -0.09 0.54
sounds -0.08 0.03 0.33 0.09 0.19 -0.05 0.16 -0.05 0.17 -0.14 0.17 -0.13 -0.04 0.28
temperature -0.09 0.27 0.54 -0.43 -0.08 0.29 -0.15 -0.09 -0.12 -0.06 -0.06 0.02 0.04 0.71
thoughts 0.57 0.10 0.18 0.05 -0.01 -0.11 -0.16 0.04 -0.03 0.00 0.09 -0.13 0.15 0.46
tired 0.45 -0.28 0.02 -0.11 -0.05 0.23 -0.10 0.21 0.09 -0.16 -0.04 -0.15 -0.01 0.45
u2 com
angry 0.59 1.4
beliefs 0.57 3.0
calm 0.61 1.7
choices 0.47 4.0
communicating 0.66 4.6
computations 0.26 1.4
conscious 0.43 4.3
depressed 0.36 1.7
depth 0.60 4.4
desires 0.58 2.2
disrespected 0.41 1.7
embarrassed 0.38 3.8
emo_recog 0.54 2.3
fear 0.33 2.7
free_will 0.49 3.6
goal 0.64 4.3
guilt 0.45 3.2
happy 0.31 2.0
hungry 0.20 2.1
intentions 0.47 4.9
joy 0.35 1.9
love 0.49 2.2
morality 0.32 4.3
nauseated 0.59 4.0
odors 0.33 3.8
pain 0.29 2.5
personality 0.48 3.7
pleasure 0.41 2.7
pride 0.40 2.0
reasoning 0.63 5.6
recognizing 0.71 4.2
remembering 0.39 3.4
safe 0.20 3.0
seeing 0.76 8.8
self_aware 0.48 4.4
self_restraint 0.46 6.4
sounds 0.72 5.0
temperature 0.29 3.7
thoughts 0.54 1.9
tired 0.55 4.0
MR1 MR2 MR3 MR5 MR6 MR4 MR7 MR8 MR9 MR11 MR10 MR12 MR13
SS loadings 8.10 4.24 2.16 1.00 0.89 0.82 0.78 0.70 0.64 0.53 0.52 0.46 0.44
Proportion Var 0.20 0.11 0.05 0.02 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01
Cumulative Var 0.20 0.31 0.36 0.39 0.41 0.43 0.45 0.47 0.48 0.50 0.51 0.52 0.53
Proportion Explained 0.38 0.20 0.10 0.05 0.04 0.04 0.04 0.03 0.03 0.02 0.02 0.02 0.02
Cumulative Proportion 0.38 0.58 0.68 0.73 0.77 0.81 0.85 0.88 0.91 0.93 0.96 0.98 1.00
Mean item complexity = 3.4
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 17.09 with Chi Square of 3159.59
The degrees of freedom for the model are 338 and the objective function was 1.74
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 198 with the empirical chi square 157.13 with prob < 1
The total number of observations was 200 with MLE Chi Square = 306.92 with prob < 0.89
Tucker Lewis Index of factoring reliability = 1.032
RMSEA index = 0.011 and the 90 % confidence intervals are NA 0.013
BIC = -1483.91
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR3 MR5 MR6 MR4 MR7 MR8 MR9 MR11
Correlation of scores with factors 0.98 0.96 0.92 0.84 0.83 0.84 0.81 0.78 0.76 0.73
Multiple R square of scores with factors 0.95 0.93 0.84 0.70 0.68 0.71 0.65 0.61 0.58 0.54
Minimum correlation of possible factor scores 0.91 0.85 0.69 0.41 0.37 0.42 0.30 0.22 0.17 0.08
MR10 MR12 MR13
Correlation of scores with factors 0.75 0.71 0.69
Multiple R square of scores with factors 0.56 0.50 0.48
Minimum correlation of possible factor scores 0.12 0.00 -0.05# count factors with eigenvalues > 1 and variance explained > 5%
efa_child_max_unrot_nfactors <- efa_child_max_unrot_eigenvalues %>%
filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
count() %>%
as.numeric()
efa_child_max_unrot_nfactors[1] 3# manually check that each factor is the dominant factor for at least one mental capacity item
efa_child_max_unrot_loadings <- fa.sort(loadings(efa_child_max_unrot)[]) %>%
data.frame() %>%
select(1:efa_child_max_unrot_nfactors) %>%
rename(F1 = MR1, F2 = MR2, F3 = MR3) %>% # adjust by hand as needed
mutate(F1_abs = abs(F1),
F2_abs = abs(F2),
F3_abs = abs(F3),
loading_abs = pmax(F1_abs, F2_abs, F3_abs),
loading = ifelse(loading_abs == abs(F1), F1,
ifelse(loading_abs == abs(F2), F2,
ifelse(loading_abs == abs(F3), F3,
NA))),
factor = ifelse(loading == F1, "F1",
ifelse(loading == F2, "F2",
ifelse(loading == F3, "F3",
NA))),
factorName = ifelse(loading == F1, "Factor 1",
ifelse(loading == F2, "Factor 2",
ifelse(loading == F3, "Factor 3",
NA))))
efa_child_max_unrot_loadings %>% count(factorName) # drop any factors where n < 1# reset as needed
efa_child_max_unrot_nfactors <- efa_child_max_unrot_loadings %>% count(factorName) %>% nrow()Maximal (13-factor) rotated solution
# do factor analysis
efa_child_max_rot <- fa(d3_child, nfactors = 13, rotate = rot_type, cor = cor_type)
efa_child_max_rotFactor Analysis using method = minres
Call: fa(r = d3_child, nfactors = 13, rotate = rot_type, cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR5 MR8 MR6 MR3 MR7 MR12 MR10 MR13 MR9 MR4 MR11 h2
angry 0.35 0.31 0.19 0.11 0.14 -0.14 0.28 0.11 -0.01 0.02 0.01 0.06 0.11 0.41
beliefs 0.39 -0.05 0.35 0.04 0.21 0.07 0.27 -0.01 -0.02 0.16 -0.03 -0.03 0.01 0.43
calm 0.50 0.16 0.07 0.11 0.01 0.06 0.00 0.12 -0.14 0.19 0.03 0.09 0.09 0.39
choices 0.15 0.12 0.65 0.13 0.05 -0.04 0.04 0.18 0.07 0.03 0.07 -0.03 0.04 0.53
communicating 0.12 -0.12 -0.08 0.09 0.00 0.07 0.00 -0.02 0.41 0.12 0.32 -0.01 -0.08 0.34
computations 0.06 -0.67 0.10 -0.02 0.23 0.29 0.10 0.11 0.17 0.12 0.18 -0.12 0.16 0.74
conscious 0.20 0.12 0.05 0.65 -0.04 0.19 0.04 -0.02 0.07 0.08 0.16 0.02 -0.11 0.57
depressed 0.59 0.34 0.05 0.05 0.08 -0.11 0.07 0.22 0.21 0.13 -0.03 -0.01 0.20 0.64
depth -0.03 0.03 0.08 0.11 0.12 0.53 0.16 0.07 -0.02 0.12 0.11 0.07 -0.14 0.40
desires 0.39 0.26 0.24 0.06 -0.08 -0.09 0.21 -0.10 -0.09 0.24 -0.05 0.08 -0.04 0.42
disrespected 0.59 0.18 0.18 0.19 0.17 -0.07 0.21 0.15 -0.03 -0.05 -0.07 -0.13 -0.08 0.59
embarrassed 0.37 0.10 0.04 0.06 0.11 -0.07 0.64 0.00 0.05 0.02 -0.05 -0.06 0.17 0.62
emo_recog 0.25 -0.21 -0.04 0.20 0.46 0.11 0.16 0.13 0.20 0.06 0.00 -0.10 0.02 0.46
fear 0.32 0.55 0.00 0.11 -0.05 -0.11 0.15 0.08 0.11 0.22 0.01 0.38 -0.02 0.67
free_will 0.27 0.17 0.54 0.14 -0.06 0.13 0.03 0.15 0.01 0.12 0.04 0.15 -0.12 0.51
goal 0.25 -0.15 0.14 0.11 0.12 -0.03 0.08 0.14 0.08 0.39 0.02 0.02 0.19 0.36
guilt 0.47 0.08 0.06 0.00 0.11 0.19 0.51 0.02 -0.07 0.04 -0.03 0.08 0.01 0.55
happy 0.79 0.05 0.08 0.05 0.05 0.00 0.14 0.05 0.15 -0.03 0.05 0.07 0.00 0.69
hungry 0.15 0.84 0.12 0.04 -0.13 0.01 0.01 -0.02 -0.16 -0.11 -0.03 0.05 0.02 0.80
intentions 0.19 -0.20 0.31 0.11 0.09 0.23 0.18 0.48 0.09 -0.06 0.07 0.09 -0.03 0.53
joy 0.77 0.12 0.10 0.00 0.11 -0.08 0.07 0.01 0.05 0.03 0.02 -0.06 0.01 0.65
love 0.59 0.14 0.02 0.10 0.13 0.08 0.18 0.04 0.02 0.00 -0.24 -0.11 -0.03 0.51
morality 0.31 -0.09 0.03 -0.03 0.70 0.10 0.06 0.15 0.01 0.09 -0.11 0.13 -0.12 0.68
nauseated 0.08 0.59 0.04 -0.09 0.03 0.07 0.15 -0.04 0.02 0.02 -0.03 0.00 0.14 0.41
odors -0.05 0.61 0.10 0.01 -0.03 0.04 0.01 -0.09 -0.20 -0.05 0.47 0.07 -0.10 0.67
pain 0.22 0.75 0.22 0.07 -0.01 -0.02 -0.07 -0.04 -0.20 0.02 -0.06 -0.02 -0.05 0.71
personality 0.27 0.02 0.25 0.10 0.41 0.05 0.28 0.15 0.07 0.27 0.01 -0.04 0.17 0.52
pleasure 0.70 0.04 0.07 -0.04 0.05 0.02 -0.08 -0.01 -0.06 0.21 -0.01 0.18 0.01 0.59
pride 0.67 -0.01 0.08 0.02 0.17 -0.05 0.24 0.11 0.09 0.00 -0.09 0.11 0.11 0.60
reasoning 0.10 -0.04 0.19 0.26 0.07 0.17 -0.09 0.02 0.21 0.36 0.08 -0.03 -0.18 0.37
recognizing 0.11 -0.10 -0.01 0.02 0.22 0.23 0.07 0.06 0.08 0.34 0.19 0.03 -0.02 0.29
remembering 0.01 -0.31 0.22 0.08 0.16 0.22 0.01 -0.03 0.59 0.08 -0.07 0.02 -0.15 0.61
safe 0.37 0.26 0.24 0.34 0.29 0.08 -0.09 -0.06 -0.05 -0.04 0.08 0.51 0.23 0.80
seeing -0.08 -0.07 0.03 0.16 0.04 0.01 -0.11 0.06 0.13 -0.02 0.09 -0.03 -0.40 0.24
self_aware 0.02 -0.02 0.15 0.67 0.09 0.07 0.06 0.13 0.04 0.06 0.07 0.05 -0.06 0.52
self_restraint 0.15 0.04 0.17 0.08 0.17 0.03 -0.04 0.65 -0.06 0.13 -0.05 -0.04 -0.06 0.54
sounds -0.13 -0.03 0.07 0.17 -0.04 0.05 -0.05 0.00 0.06 0.06 0.46 0.00 -0.04 0.28
temperature -0.08 -0.07 -0.03 0.16 0.01 0.76 -0.16 0.03 0.20 -0.02 -0.03 -0.07 0.12 0.71
thoughts 0.43 0.20 0.25 0.28 0.24 0.03 -0.13 0.07 -0.01 0.09 0.05 -0.02 0.05 0.46
tired 0.23 0.55 -0.09 0.08 0.07 0.01 0.04 0.16 0.10 -0.07 0.01 -0.13 0.11 0.45
u2 com
angry 0.59 5.1
beliefs 0.57 4.0
calm 0.61 2.3
choices 0.47 1.6
communicating 0.66 2.9
computations 0.26 2.6
conscious 0.43 1.8
depressed 0.36 2.9
depth 0.60 2.0
desires 0.58 5.0
disrespected 0.41 2.6
embarrassed 0.38 2.1
emo_recog 0.54 4.0
fear 0.33 3.5
free_will 0.49 2.8
goal 0.64 4.2
guilt 0.45 2.6
happy 0.31 1.2
hungry 0.20 1.3
intentions 0.47 4.1
joy 0.35 1.2
love 0.49 2.0
morality 0.32 1.9
nauseated 0.59 1.4
odors 0.33 2.4
pain 0.29 1.6
personality 0.48 5.4
pleasure 0.41 1.4
pride 0.40 1.7
reasoning 0.63 5.1
recognizing 0.71 4.3
remembering 0.39 2.7
safe 0.20 5.5
seeing 0.76 2.2
self_aware 0.48 1.4
self_restraint 0.46 1.6
sounds 0.72 1.7
temperature 0.29 1.5
thoughts 0.54 4.2
tired 0.55 2.1
MR1 MR2 MR5 MR8 MR6 MR3 MR7 MR12 MR10 MR13 MR9 MR4 MR11
SS loadings 5.37 3.84 1.55 1.48 1.44 1.36 1.35 1.01 0.98 0.85 0.78 0.63 0.62
Proportion Var 0.13 0.10 0.04 0.04 0.04 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02
Cumulative Var 0.13 0.23 0.27 0.31 0.34 0.38 0.41 0.44 0.46 0.48 0.50 0.52 0.53
Proportion Explained 0.25 0.18 0.07 0.07 0.07 0.06 0.06 0.05 0.05 0.04 0.04 0.03 0.03
Cumulative Proportion 0.25 0.43 0.51 0.58 0.64 0.71 0.77 0.82 0.86 0.90 0.94 0.97 1.00
Mean item complexity = 2.7
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 17.09 with Chi Square of 3159.59
The degrees of freedom for the model are 338 and the objective function was 1.74
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 198 with the empirical chi square 157.13 with prob < 1
The total number of observations was 200 with MLE Chi Square = 306.92 with prob < 0.89
Tucker Lewis Index of factoring reliability = 1.032
RMSEA index = 0.011 and the 90 % confidence intervals are NA 0.013
BIC = -1483.91
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR5 MR8 MR6 MR3 MR7 MR12 MR10 MR13
Correlation of scores with factors 0.93 0.94 0.82 0.83 0.83 0.86 0.83 0.78 0.78 0.73
Multiple R square of scores with factors 0.86 0.88 0.67 0.69 0.68 0.74 0.68 0.61 0.61 0.54
Minimum correlation of possible factor scores 0.72 0.76 0.34 0.38 0.36 0.47 0.37 0.23 0.23 0.08
MR9 MR4 MR11
Correlation of scores with factors 0.77 0.79 0.75
Multiple R square of scores with factors 0.59 0.62 0.56
Minimum correlation of possible factor scores 0.19 0.24 0.12# examine eigenvalues and variance explained
efa_child_max_rot_eigenvalues <- print(efa_child_max_rot)$Vaccounted %>%
t() %>%
data.frame()Factor Analysis using method = minres
Call: fa(r = d3_child, nfactors = 13, rotate = rot_type, cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR5 MR8 MR6 MR3 MR7 MR12 MR10 MR13 MR9 MR4 MR11 h2
angry 0.35 0.31 0.19 0.11 0.14 -0.14 0.28 0.11 -0.01 0.02 0.01 0.06 0.11 0.41
beliefs 0.39 -0.05 0.35 0.04 0.21 0.07 0.27 -0.01 -0.02 0.16 -0.03 -0.03 0.01 0.43
calm 0.50 0.16 0.07 0.11 0.01 0.06 0.00 0.12 -0.14 0.19 0.03 0.09 0.09 0.39
choices 0.15 0.12 0.65 0.13 0.05 -0.04 0.04 0.18 0.07 0.03 0.07 -0.03 0.04 0.53
communicating 0.12 -0.12 -0.08 0.09 0.00 0.07 0.00 -0.02 0.41 0.12 0.32 -0.01 -0.08 0.34
computations 0.06 -0.67 0.10 -0.02 0.23 0.29 0.10 0.11 0.17 0.12 0.18 -0.12 0.16 0.74
conscious 0.20 0.12 0.05 0.65 -0.04 0.19 0.04 -0.02 0.07 0.08 0.16 0.02 -0.11 0.57
depressed 0.59 0.34 0.05 0.05 0.08 -0.11 0.07 0.22 0.21 0.13 -0.03 -0.01 0.20 0.64
depth -0.03 0.03 0.08 0.11 0.12 0.53 0.16 0.07 -0.02 0.12 0.11 0.07 -0.14 0.40
desires 0.39 0.26 0.24 0.06 -0.08 -0.09 0.21 -0.10 -0.09 0.24 -0.05 0.08 -0.04 0.42
disrespected 0.59 0.18 0.18 0.19 0.17 -0.07 0.21 0.15 -0.03 -0.05 -0.07 -0.13 -0.08 0.59
embarrassed 0.37 0.10 0.04 0.06 0.11 -0.07 0.64 0.00 0.05 0.02 -0.05 -0.06 0.17 0.62
emo_recog 0.25 -0.21 -0.04 0.20 0.46 0.11 0.16 0.13 0.20 0.06 0.00 -0.10 0.02 0.46
fear 0.32 0.55 0.00 0.11 -0.05 -0.11 0.15 0.08 0.11 0.22 0.01 0.38 -0.02 0.67
free_will 0.27 0.17 0.54 0.14 -0.06 0.13 0.03 0.15 0.01 0.12 0.04 0.15 -0.12 0.51
goal 0.25 -0.15 0.14 0.11 0.12 -0.03 0.08 0.14 0.08 0.39 0.02 0.02 0.19 0.36
guilt 0.47 0.08 0.06 0.00 0.11 0.19 0.51 0.02 -0.07 0.04 -0.03 0.08 0.01 0.55
happy 0.79 0.05 0.08 0.05 0.05 0.00 0.14 0.05 0.15 -0.03 0.05 0.07 0.00 0.69
hungry 0.15 0.84 0.12 0.04 -0.13 0.01 0.01 -0.02 -0.16 -0.11 -0.03 0.05 0.02 0.80
intentions 0.19 -0.20 0.31 0.11 0.09 0.23 0.18 0.48 0.09 -0.06 0.07 0.09 -0.03 0.53
joy 0.77 0.12 0.10 0.00 0.11 -0.08 0.07 0.01 0.05 0.03 0.02 -0.06 0.01 0.65
love 0.59 0.14 0.02 0.10 0.13 0.08 0.18 0.04 0.02 0.00 -0.24 -0.11 -0.03 0.51
morality 0.31 -0.09 0.03 -0.03 0.70 0.10 0.06 0.15 0.01 0.09 -0.11 0.13 -0.12 0.68
nauseated 0.08 0.59 0.04 -0.09 0.03 0.07 0.15 -0.04 0.02 0.02 -0.03 0.00 0.14 0.41
odors -0.05 0.61 0.10 0.01 -0.03 0.04 0.01 -0.09 -0.20 -0.05 0.47 0.07 -0.10 0.67
pain 0.22 0.75 0.22 0.07 -0.01 -0.02 -0.07 -0.04 -0.20 0.02 -0.06 -0.02 -0.05 0.71
personality 0.27 0.02 0.25 0.10 0.41 0.05 0.28 0.15 0.07 0.27 0.01 -0.04 0.17 0.52
pleasure 0.70 0.04 0.07 -0.04 0.05 0.02 -0.08 -0.01 -0.06 0.21 -0.01 0.18 0.01 0.59
pride 0.67 -0.01 0.08 0.02 0.17 -0.05 0.24 0.11 0.09 0.00 -0.09 0.11 0.11 0.60
reasoning 0.10 -0.04 0.19 0.26 0.07 0.17 -0.09 0.02 0.21 0.36 0.08 -0.03 -0.18 0.37
recognizing 0.11 -0.10 -0.01 0.02 0.22 0.23 0.07 0.06 0.08 0.34 0.19 0.03 -0.02 0.29
remembering 0.01 -0.31 0.22 0.08 0.16 0.22 0.01 -0.03 0.59 0.08 -0.07 0.02 -0.15 0.61
safe 0.37 0.26 0.24 0.34 0.29 0.08 -0.09 -0.06 -0.05 -0.04 0.08 0.51 0.23 0.80
seeing -0.08 -0.07 0.03 0.16 0.04 0.01 -0.11 0.06 0.13 -0.02 0.09 -0.03 -0.40 0.24
self_aware 0.02 -0.02 0.15 0.67 0.09 0.07 0.06 0.13 0.04 0.06 0.07 0.05 -0.06 0.52
self_restraint 0.15 0.04 0.17 0.08 0.17 0.03 -0.04 0.65 -0.06 0.13 -0.05 -0.04 -0.06 0.54
sounds -0.13 -0.03 0.07 0.17 -0.04 0.05 -0.05 0.00 0.06 0.06 0.46 0.00 -0.04 0.28
temperature -0.08 -0.07 -0.03 0.16 0.01 0.76 -0.16 0.03 0.20 -0.02 -0.03 -0.07 0.12 0.71
thoughts 0.43 0.20 0.25 0.28 0.24 0.03 -0.13 0.07 -0.01 0.09 0.05 -0.02 0.05 0.46
tired 0.23 0.55 -0.09 0.08 0.07 0.01 0.04 0.16 0.10 -0.07 0.01 -0.13 0.11 0.45
u2 com
angry 0.59 5.1
beliefs 0.57 4.0
calm 0.61 2.3
choices 0.47 1.6
communicating 0.66 2.9
computations 0.26 2.6
conscious 0.43 1.8
depressed 0.36 2.9
depth 0.60 2.0
desires 0.58 5.0
disrespected 0.41 2.6
embarrassed 0.38 2.1
emo_recog 0.54 4.0
fear 0.33 3.5
free_will 0.49 2.8
goal 0.64 4.2
guilt 0.45 2.6
happy 0.31 1.2
hungry 0.20 1.3
intentions 0.47 4.1
joy 0.35 1.2
love 0.49 2.0
morality 0.32 1.9
nauseated 0.59 1.4
odors 0.33 2.4
pain 0.29 1.6
personality 0.48 5.4
pleasure 0.41 1.4
pride 0.40 1.7
reasoning 0.63 5.1
recognizing 0.71 4.3
remembering 0.39 2.7
safe 0.20 5.5
seeing 0.76 2.2
self_aware 0.48 1.4
self_restraint 0.46 1.6
sounds 0.72 1.7
temperature 0.29 1.5
thoughts 0.54 4.2
tired 0.55 2.1
MR1 MR2 MR5 MR8 MR6 MR3 MR7 MR12 MR10 MR13 MR9 MR4 MR11
SS loadings 5.37 3.84 1.55 1.48 1.44 1.36 1.35 1.01 0.98 0.85 0.78 0.63 0.62
Proportion Var 0.13 0.10 0.04 0.04 0.04 0.03 0.03 0.03 0.02 0.02 0.02 0.02 0.02
Cumulative Var 0.13 0.23 0.27 0.31 0.34 0.38 0.41 0.44 0.46 0.48 0.50 0.52 0.53
Proportion Explained 0.25 0.18 0.07 0.07 0.07 0.06 0.06 0.05 0.05 0.04 0.04 0.03 0.03
Cumulative Proportion 0.25 0.43 0.51 0.58 0.64 0.71 0.77 0.82 0.86 0.90 0.94 0.97 1.00
Mean item complexity = 2.7
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 17.09 with Chi Square of 3159.59
The degrees of freedom for the model are 338 and the objective function was 1.74
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.03
The harmonic number of observations is 198 with the empirical chi square 157.13 with prob < 1
The total number of observations was 200 with MLE Chi Square = 306.92 with prob < 0.89
Tucker Lewis Index of factoring reliability = 1.032
RMSEA index = 0.011 and the 90 % confidence intervals are NA 0.013
BIC = -1483.91
Fit based upon off diagonal values = 0.99
Measures of factor score adequacy
MR1 MR2 MR5 MR8 MR6 MR3 MR7 MR12 MR10 MR13
Correlation of scores with factors 0.93 0.94 0.82 0.83 0.83 0.86 0.83 0.78 0.78 0.73
Multiple R square of scores with factors 0.86 0.88 0.67 0.69 0.68 0.74 0.68 0.61 0.61 0.54
Minimum correlation of possible factor scores 0.72 0.76 0.34 0.38 0.36 0.47 0.37 0.23 0.23 0.08
MR9 MR4 MR11
Correlation of scores with factors 0.77 0.79 0.75
Multiple R square of scores with factors 0.59 0.62 0.56
Minimum correlation of possible factor scores 0.19 0.24 0.12efa_child_max_rot_eigenvalues# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"Small rotated solution
# do factor analysis
efa_child_small_rot <- fa(d3_child, nfactors = efa_child_max_unrot_nfactors, rotate = rot_type, cor = cor_type)
efa_child_small_rotFactor Analysis using method = minres
Call: fa(r = d3_child, nfactors = efa_child_max_unrot_nfactors, rotate = rot_type,
cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 h2 u2 com
angry 0.50 0.31 0.05 0.350 0.65 1.7
beliefs 0.53 -0.04 0.22 0.329 0.67 1.3
calm 0.48 0.22 0.13 0.298 0.70 1.6
choices 0.28 0.19 0.36 0.241 0.76 2.4
communicating 0.08 -0.18 0.24 0.099 0.90 2.1
computations 0.14 -0.71 0.34 0.637 0.36 1.5
conscious 0.17 0.20 0.49 0.309 0.69 1.6
depressed 0.66 0.27 0.02 0.505 0.50 1.3
depth 0.02 0.01 0.44 0.198 0.80 1.0
desires 0.43 0.33 0.03 0.296 0.70 1.9
disrespected 0.67 0.19 0.10 0.488 0.51 1.2
embarrassed 0.57 0.04 -0.06 0.329 0.67 1.0
emo_recog 0.40 -0.31 0.28 0.340 0.66 2.7
fear 0.39 0.53 0.06 0.438 0.56 1.9
free_will 0.31 0.28 0.40 0.338 0.66 2.7
goal 0.37 -0.17 0.22 0.218 0.78 2.1
guilt 0.59 0.06 0.06 0.352 0.65 1.0
happy 0.76 0.07 0.04 0.582 0.42 1.0
hungry 0.12 0.87 -0.07 0.776 0.22 1.1
intentions 0.32 -0.18 0.41 0.297 0.70 2.3
joy 0.75 0.12 -0.04 0.577 0.42 1.1
love 0.63 0.11 0.00 0.414 0.59 1.1
morality 0.47 -0.18 0.20 0.293 0.71 1.7
nauseated 0.16 0.51 -0.06 0.287 0.71 1.2
odors -0.10 0.63 0.11 0.425 0.58 1.1
pain 0.21 0.79 0.01 0.664 0.34 1.1
personality 0.51 -0.06 0.30 0.359 0.64 1.7
pleasure 0.62 0.09 0.02 0.394 0.61 1.0
pride 0.77 -0.03 -0.02 0.602 0.40 1.0
reasoning 0.12 -0.04 0.49 0.259 0.74 1.1
recognizing 0.18 -0.17 0.32 0.160 0.84 2.1
remembering 0.10 -0.40 0.39 0.317 0.68 2.1
safe 0.42 0.33 0.31 0.380 0.62 2.8
seeing -0.13 -0.05 0.23 0.072 0.93 1.7
self_aware 0.11 0.06 0.52 0.288 0.71 1.1
self_restraint 0.26 0.02 0.28 0.146 0.85 2.0
sounds -0.17 0.01 0.33 0.139 0.86 1.5
temperature -0.12 -0.13 0.42 0.211 0.79 1.4
thoughts 0.46 0.24 0.32 0.367 0.63 2.4
tired 0.27 0.46 -0.01 0.286 0.71 1.6
MR1 MR2 MR3
SS loadings 7.03 4.20 2.83
Proportion Var 0.18 0.10 0.07
Cumulative Var 0.18 0.28 0.35
Proportion Explained 0.50 0.30 0.20
Cumulative Proportion 0.50 0.80 1.00
Mean item complexity = 1.6
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 17.09 with Chi Square of 3159.59
The degrees of freedom for the model are 663 and the objective function was 4.8
The root mean square of the residuals (RMSR) is 0.05
The df corrected root mean square of the residuals is 0.06
The harmonic number of observations is 198 with the empirical chi square 808.39 with prob < 8.8e-05
The total number of observations was 200 with MLE Chi Square = 877.46 with prob < 3.9e-08
Tucker Lewis Index of factoring reliability = 0.892
RMSEA index = 0.047 and the 90 % confidence intervals are 0.033 0.047
BIC = -2635.32
Fit based upon off diagonal values = 0.95
Measures of factor score adequacy
MR1 MR2 MR3
Correlation of scores with factors 0.96 0.95 0.88
Multiple R square of scores with factors 0.91 0.90 0.78
Minimum correlation of possible factor scores 0.83 0.80 0.55# examine eigenvalues and variance explained
efa_child_small_rot_eigenvalues <- print(efa_child_small_rot)$Vaccounted %>%
t() %>%
data.frame()Factor Analysis using method = minres
Call: fa(r = d3_child, nfactors = efa_child_max_unrot_nfactors, rotate = rot_type,
cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 h2 u2 com
angry 0.50 0.31 0.05 0.350 0.65 1.7
beliefs 0.53 -0.04 0.22 0.329 0.67 1.3
calm 0.48 0.22 0.13 0.298 0.70 1.6
choices 0.28 0.19 0.36 0.241 0.76 2.4
communicating 0.08 -0.18 0.24 0.099 0.90 2.1
computations 0.14 -0.71 0.34 0.637 0.36 1.5
conscious 0.17 0.20 0.49 0.309 0.69 1.6
depressed 0.66 0.27 0.02 0.505 0.50 1.3
depth 0.02 0.01 0.44 0.198 0.80 1.0
desires 0.43 0.33 0.03 0.296 0.70 1.9
disrespected 0.67 0.19 0.10 0.488 0.51 1.2
embarrassed 0.57 0.04 -0.06 0.329 0.67 1.0
emo_recog 0.40 -0.31 0.28 0.340 0.66 2.7
fear 0.39 0.53 0.06 0.438 0.56 1.9
free_will 0.31 0.28 0.40 0.338 0.66 2.7
goal 0.37 -0.17 0.22 0.218 0.78 2.1
guilt 0.59 0.06 0.06 0.352 0.65 1.0
happy 0.76 0.07 0.04 0.582 0.42 1.0
hungry 0.12 0.87 -0.07 0.776 0.22 1.1
intentions 0.32 -0.18 0.41 0.297 0.70 2.3
joy 0.75 0.12 -0.04 0.577 0.42 1.1
love 0.63 0.11 0.00 0.414 0.59 1.1
morality 0.47 -0.18 0.20 0.293 0.71 1.7
nauseated 0.16 0.51 -0.06 0.287 0.71 1.2
odors -0.10 0.63 0.11 0.425 0.58 1.1
pain 0.21 0.79 0.01 0.664 0.34 1.1
personality 0.51 -0.06 0.30 0.359 0.64 1.7
pleasure 0.62 0.09 0.02 0.394 0.61 1.0
pride 0.77 -0.03 -0.02 0.602 0.40 1.0
reasoning 0.12 -0.04 0.49 0.259 0.74 1.1
recognizing 0.18 -0.17 0.32 0.160 0.84 2.1
remembering 0.10 -0.40 0.39 0.317 0.68 2.1
safe 0.42 0.33 0.31 0.380 0.62 2.8
seeing -0.13 -0.05 0.23 0.072 0.93 1.7
self_aware 0.11 0.06 0.52 0.288 0.71 1.1
self_restraint 0.26 0.02 0.28 0.146 0.85 2.0
sounds -0.17 0.01 0.33 0.139 0.86 1.5
temperature -0.12 -0.13 0.42 0.211 0.79 1.4
thoughts 0.46 0.24 0.32 0.367 0.63 2.4
tired 0.27 0.46 -0.01 0.286 0.71 1.6
MR1 MR2 MR3
SS loadings 7.03 4.20 2.83
Proportion Var 0.18 0.10 0.07
Cumulative Var 0.18 0.28 0.35
Proportion Explained 0.50 0.30 0.20
Cumulative Proportion 0.50 0.80 1.00
Mean item complexity = 1.6
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 17.09 with Chi Square of 3159.59
The degrees of freedom for the model are 663 and the objective function was 4.8
The root mean square of the residuals (RMSR) is 0.05
The df corrected root mean square of the residuals is 0.06
The harmonic number of observations is 198 with the empirical chi square 808.39 with prob < 8.8e-05
The total number of observations was 200 with MLE Chi Square = 877.46 with prob < 3.9e-08
Tucker Lewis Index of factoring reliability = 0.892
RMSEA index = 0.047 and the 90 % confidence intervals are 0.033 0.047
BIC = -2635.32
Fit based upon off diagonal values = 0.95
Measures of factor score adequacy
MR1 MR2 MR3
Correlation of scores with factors 0.96 0.95 0.88
Multiple R square of scores with factors 0.91 0.90 0.78
Minimum correlation of possible factor scores 0.83 0.80 0.55efa_child_small_rot_eigenvalues# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"Loadings
# make dataframe for all factor loadings and dominant factor
efa_child_small_rot_loadings <- efa_child_small_rot$loadings[] %>%
fa.sort() %>%
data.frame() %>%
rownames_to_column(var = "capacity") %>%
rename(F1 = MR1, F2 = MR2, F3 = MR3) %>% # adjust by hand as needed
mutate(F1_abs = abs(F1),
F2_abs = abs(F2),
F3_abs = abs(F3),
loading_abs = pmax(F1_abs, F2_abs, F3_abs),
loading = ifelse(loading_abs == abs(F1), F1,
ifelse(loading_abs == abs(F2), F2,
ifelse(loading_abs == abs(F3), F3,
NA))),
factor = ifelse(loading == F1, "F1",
ifelse(loading == F2, "F2",
ifelse(loading == F3, "F3",
NA))),
factorName = ifelse(loading == F1, "Factor 1",
ifelse(loading == F2, "Factor 2",
ifelse(loading == F3, "Factor 3",
NA)))) %>%
select(capacity, F1, F2, F3, factor, factorName, loading, loading_abs) %>%
distinct() %>%
full_join(d %>% select(capacity, capWording)) %>%
mutate(capWording = gsub("\\ ", "_", gsub(" --.*", "", capWording))) %>%
select(capacity, capWording, F1:loading_abs) %>%
distinct()Joining, by = "capacity"
joining factor and character vector, coercing into character vector# print out list of items by dominant factor
efa_child_small_rot_items <- efa_child_small_rot_loadings %>%
arrange(factor, desc(loading_abs)) %>%
mutate(capWordingPlus = ifelse(loading > 0, capWording,
paste0(capWording, " (negative loading)"))) %>%
group_by(factor) %>%
summarise(items = gsub("_", " ", paste(capWordingPlus, collapse = ", ")))
head(efa_child_small_rot_items$items, 40)[1] "feel proud, feel happy, feel joy, get hurt feelings, feel sad, feel love, feel pleasure, feel guilty, feel embarrassed, have beliefs, have a personality, get angry, feel calm, know what's nice and what's mean, have thoughts, have desires, feel safe, understand how somebody else is feeling, have goals"
[2] "get hungry, feel pain, do math (negative loading), smell things, feel scared, feel sick, feel tired, remember things (negative loading)"
[3] "be aware of itself, figure out how to do things, be aware of things, sense whether something is close by or far away, sense temperatures, make plans, decide what to do, make choices, hear sounds, recognize somebody else, have self-control, communicate with somebody else, see things" Combined dataset: Children & adults
Exploratory factor analysis
Maximal (13-factor) unrotated solution
# do factor analysis
efa_comb_max_unrot <- fa(d3_combined, nfactors = 13, rotate = "none", cor = cor_type)
efa_comb_max_unrotFactor Analysis using method = minres
Call: fa(r = d3_combined, nfactors = 13, rotate = "none", cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12 MR13 h2
angry 0.73 0.04 -0.06 -0.12 0.07 0.03 0.11 0.00 0.13 -0.18 -0.07 0.07 0.04 0.63
beliefs 0.58 0.36 0.01 -0.13 0.12 -0.10 0.02 0.11 -0.12 0.12 0.03 -0.02 -0.07 0.55
calm 0.70 -0.02 0.04 0.04 -0.09 -0.05 0.01 0.03 -0.03 -0.08 0.08 0.08 -0.02 0.53
choices 0.30 0.15 0.42 -0.30 -0.02 0.15 -0.13 0.04 0.04 -0.01 0.02 0.02 -0.05 0.42
communicating 0.07 0.42 0.29 0.19 -0.12 0.32 0.21 -0.02 0.14 -0.03 0.00 -0.11 0.01 0.50
computations -0.29 0.82 0.13 0.08 0.10 0.02 -0.04 0.06 -0.02 -0.11 0.09 0.10 -0.08 0.83
conscious 0.23 -0.02 0.52 0.00 0.01 -0.10 0.04 -0.31 0.02 0.00 0.14 -0.06 -0.02 0.46
depressed 0.78 0.13 -0.13 0.06 -0.09 0.05 -0.05 0.02 0.14 -0.06 -0.02 0.04 -0.07 0.69
depth -0.08 0.08 0.52 0.17 0.30 -0.04 -0.14 -0.10 -0.09 -0.03 -0.03 -0.05 0.12 0.46
desires 0.67 -0.09 0.04 -0.09 0.06 0.20 0.13 0.06 -0.29 0.24 0.06 0.09 0.02 0.68
disrespected 0.75 0.19 -0.15 -0.06 0.04 -0.02 -0.09 -0.11 0.13 0.07 0.04 -0.07 0.05 0.68
embarrassed 0.59 0.23 -0.22 -0.05 0.39 0.10 0.13 -0.11 0.07 -0.03 -0.03 -0.04 -0.11 0.67
emo_recog 0.38 0.46 0.07 0.05 0.02 -0.25 0.07 0.00 0.06 0.11 -0.03 -0.12 -0.03 0.46
fear 0.71 -0.32 0.11 0.10 -0.07 0.04 0.19 0.04 -0.04 -0.14 -0.12 -0.01 0.04 0.71
free_will 0.30 0.09 0.46 -0.43 -0.04 0.20 -0.23 -0.03 -0.08 -0.13 -0.09 -0.04 0.00 0.63
goal 0.40 0.26 0.18 -0.07 -0.04 -0.04 0.12 0.11 -0.14 0.03 0.02 0.15 0.03 0.34
guilt 0.67 0.25 -0.16 -0.01 0.32 0.01 0.01 -0.12 -0.08 -0.04 -0.06 -0.01 0.11 0.67
happy 0.82 0.21 -0.14 0.10 -0.16 0.04 -0.07 -0.10 -0.03 -0.06 0.03 -0.01 -0.05 0.79
hungry 0.53 -0.72 0.12 0.01 0.10 0.01 -0.09 0.02 0.01 0.02 -0.02 0.01 -0.06 0.84
intentions 0.21 0.49 0.26 -0.18 0.03 -0.02 -0.04 0.04 0.05 0.03 -0.05 0.14 0.17 0.44
joy 0.80 0.19 -0.19 0.12 -0.11 0.09 -0.11 -0.01 -0.04 0.05 0.13 -0.03 0.01 0.79
love 0.73 0.19 -0.12 0.10 0.03 -0.03 -0.14 -0.08 0.02 0.15 -0.05 -0.03 -0.03 0.65
morality 0.41 0.46 0.04 0.04 0.01 -0.23 -0.04 0.22 0.04 0.03 -0.12 -0.12 0.15 0.54
nauseated 0.46 -0.38 0.15 0.16 0.13 0.11 0.00 0.15 0.03 0.05 -0.08 0.05 -0.10 0.48
odors 0.26 -0.53 0.35 0.06 0.16 0.03 0.02 0.03 0.05 -0.09 0.25 -0.13 0.09 0.60
pain 0.60 -0.54 0.15 0.00 0.03 0.05 -0.03 0.10 0.10 0.03 0.01 -0.01 0.03 0.71
personality 0.57 0.39 0.03 -0.12 0.14 -0.13 0.08 0.24 0.03 0.03 0.05 -0.12 -0.13 0.62
pleasure 0.71 0.02 -0.05 0.18 -0.13 0.00 -0.10 0.07 -0.15 -0.03 0.05 -0.06 0.05 0.60
pride 0.76 0.32 -0.23 0.01 0.00 -0.01 0.01 -0.06 -0.03 -0.06 -0.06 0.08 0.08 0.77
reasoning 0.21 0.26 0.47 -0.07 -0.13 0.09 0.04 -0.04 0.00 -0.02 -0.01 -0.09 -0.06 0.38
recognizing 0.01 0.49 0.33 0.15 0.03 0.16 0.09 0.18 0.02 0.00 0.08 -0.10 0.08 0.46
remembering 0.01 0.54 0.35 0.12 -0.13 0.16 0.05 -0.03 0.03 0.18 -0.28 -0.07 -0.05 0.59
safe 0.68 -0.17 0.26 0.01 -0.11 -0.25 0.10 -0.01 -0.19 -0.15 -0.06 -0.04 -0.06 0.72
seeing 0.12 0.03 0.38 0.11 -0.04 0.03 0.06 -0.05 0.06 0.13 -0.01 0.07 0.08 0.22
self_aware 0.39 0.13 0.37 -0.17 -0.10 -0.20 0.23 -0.25 0.09 0.17 0.09 0.08 -0.01 0.55
self_restraint 0.44 0.27 0.13 -0.16 -0.06 -0.13 -0.11 0.18 0.21 0.05 0.05 0.09 0.09 0.44
sounds -0.02 0.03 0.42 0.17 0.00 0.08 0.16 -0.02 0.01 -0.04 0.14 0.10 0.05 0.27
temperature -0.20 0.05 0.53 0.35 0.14 -0.15 -0.24 -0.06 -0.05 0.04 -0.10 0.10 -0.04 0.57
thoughts 0.62 0.13 0.20 -0.14 -0.16 -0.10 -0.06 0.03 0.09 0.04 0.06 0.02 -0.03 0.51
tired 0.64 -0.29 0.11 0.16 0.03 0.00 0.06 0.06 0.19 0.02 -0.01 0.18 -0.02 0.61
u2 com
angry 0.37 1.4
beliefs 0.45 2.3
calm 0.47 1.1
choices 0.58 3.7
communicating 0.50 4.7
computations 0.17 1.5
conscious 0.54 2.4
depressed 0.31 1.3
depth 0.54 2.5
desires 0.32 2.2
disrespected 0.32 1.5
embarrassed 0.33 2.9
emo_recog 0.54 3.1
fear 0.29 1.9
free_will 0.37 4.3
goal 0.66 3.6
guilt 0.33 2.1
happy 0.21 1.4
hungry 0.16 2.0
intentions 0.56 3.0
joy 0.21 1.5
love 0.35 1.5
morality 0.46 3.8
nauseated 0.52 3.4
odors 0.40 3.5
pain 0.29 2.3
personality 0.38 3.0
pleasure 0.40 1.4
pride 0.23 1.7
reasoning 0.62 2.5
recognizing 0.54 3.0
remembering 0.41 3.2
safe 0.28 2.2
seeing 0.78 2.1
self_aware 0.45 5.8
self_restraint 0.56 4.1
sounds 0.73 2.2
temperature 0.43 3.2
thoughts 0.49 1.8
tired 0.39 2.1
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12 MR13
SS loadings 11.01 4.45 2.92 0.86 0.65 0.63 0.49 0.49 0.41 0.34 0.33 0.28 0.21
Proportion Var 0.28 0.11 0.07 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.28 0.39 0.46 0.48 0.50 0.51 0.53 0.54 0.55 0.56 0.56 0.57 0.58
Proportion Explained 0.48 0.19 0.13 0.04 0.03 0.03 0.02 0.02 0.02 0.01 0.01 0.01 0.01
Cumulative Proportion 0.48 0.67 0.80 0.83 0.86 0.89 0.91 0.93 0.95 0.96 0.98 0.99 1.00
Mean item complexity = 2.6
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 21.99 with Chi Square of 8462.22
The degrees of freedom for the model are 338 and the objective function was 1.02
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.02
The harmonic number of observations is 394 with the empirical chi square 160.41 with prob < 1
The total number of observations was 400 with MLE Chi Square = 382.35 with prob < 0.048
Tucker Lewis Index of factoring reliability = 0.986
RMSEA index = 0.022 and the 90 % confidence intervals are 0.002 0.027
BIC = -1642.77
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10
Correlation of scores with factors 0.99 0.97 0.93 0.82 0.81 0.77 0.75 0.73 0.72 0.70
Multiple R square of scores with factors 0.97 0.94 0.87 0.67 0.65 0.60 0.56 0.53 0.52 0.49
Minimum correlation of possible factor scores 0.94 0.88 0.73 0.34 0.30 0.20 0.12 0.06 0.05 -0.02
MR11 MR12 MR13
Correlation of scores with factors 0.68 0.63 0.59
Multiple R square of scores with factors 0.46 0.39 0.35
Minimum correlation of possible factor scores -0.08 -0.21 -0.31# examine eigenvalues and variance explained
efa_comb_max_unrot_eigenvalues <- print(efa_comb_max_unrot)$Vaccounted %>%
t() %>%
data.frame()Factor Analysis using method = minres
Call: fa(r = d3_combined, nfactors = 13, rotate = "none", cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12 MR13 h2
angry 0.73 0.04 -0.06 -0.12 0.07 0.03 0.11 0.00 0.13 -0.18 -0.07 0.07 0.04 0.63
beliefs 0.58 0.36 0.01 -0.13 0.12 -0.10 0.02 0.11 -0.12 0.12 0.03 -0.02 -0.07 0.55
calm 0.70 -0.02 0.04 0.04 -0.09 -0.05 0.01 0.03 -0.03 -0.08 0.08 0.08 -0.02 0.53
choices 0.30 0.15 0.42 -0.30 -0.02 0.15 -0.13 0.04 0.04 -0.01 0.02 0.02 -0.05 0.42
communicating 0.07 0.42 0.29 0.19 -0.12 0.32 0.21 -0.02 0.14 -0.03 0.00 -0.11 0.01 0.50
computations -0.29 0.82 0.13 0.08 0.10 0.02 -0.04 0.06 -0.02 -0.11 0.09 0.10 -0.08 0.83
conscious 0.23 -0.02 0.52 0.00 0.01 -0.10 0.04 -0.31 0.02 0.00 0.14 -0.06 -0.02 0.46
depressed 0.78 0.13 -0.13 0.06 -0.09 0.05 -0.05 0.02 0.14 -0.06 -0.02 0.04 -0.07 0.69
depth -0.08 0.08 0.52 0.17 0.30 -0.04 -0.14 -0.10 -0.09 -0.03 -0.03 -0.05 0.12 0.46
desires 0.67 -0.09 0.04 -0.09 0.06 0.20 0.13 0.06 -0.29 0.24 0.06 0.09 0.02 0.68
disrespected 0.75 0.19 -0.15 -0.06 0.04 -0.02 -0.09 -0.11 0.13 0.07 0.04 -0.07 0.05 0.68
embarrassed 0.59 0.23 -0.22 -0.05 0.39 0.10 0.13 -0.11 0.07 -0.03 -0.03 -0.04 -0.11 0.67
emo_recog 0.38 0.46 0.07 0.05 0.02 -0.25 0.07 0.00 0.06 0.11 -0.03 -0.12 -0.03 0.46
fear 0.71 -0.32 0.11 0.10 -0.07 0.04 0.19 0.04 -0.04 -0.14 -0.12 -0.01 0.04 0.71
free_will 0.30 0.09 0.46 -0.43 -0.04 0.20 -0.23 -0.03 -0.08 -0.13 -0.09 -0.04 0.00 0.63
goal 0.40 0.26 0.18 -0.07 -0.04 -0.04 0.12 0.11 -0.14 0.03 0.02 0.15 0.03 0.34
guilt 0.67 0.25 -0.16 -0.01 0.32 0.01 0.01 -0.12 -0.08 -0.04 -0.06 -0.01 0.11 0.67
happy 0.82 0.21 -0.14 0.10 -0.16 0.04 -0.07 -0.10 -0.03 -0.06 0.03 -0.01 -0.05 0.79
hungry 0.53 -0.72 0.12 0.01 0.10 0.01 -0.09 0.02 0.01 0.02 -0.02 0.01 -0.06 0.84
intentions 0.21 0.49 0.26 -0.18 0.03 -0.02 -0.04 0.04 0.05 0.03 -0.05 0.14 0.17 0.44
joy 0.80 0.19 -0.19 0.12 -0.11 0.09 -0.11 -0.01 -0.04 0.05 0.13 -0.03 0.01 0.79
love 0.73 0.19 -0.12 0.10 0.03 -0.03 -0.14 -0.08 0.02 0.15 -0.05 -0.03 -0.03 0.65
morality 0.41 0.46 0.04 0.04 0.01 -0.23 -0.04 0.22 0.04 0.03 -0.12 -0.12 0.15 0.54
nauseated 0.46 -0.38 0.15 0.16 0.13 0.11 0.00 0.15 0.03 0.05 -0.08 0.05 -0.10 0.48
odors 0.26 -0.53 0.35 0.06 0.16 0.03 0.02 0.03 0.05 -0.09 0.25 -0.13 0.09 0.60
pain 0.60 -0.54 0.15 0.00 0.03 0.05 -0.03 0.10 0.10 0.03 0.01 -0.01 0.03 0.71
personality 0.57 0.39 0.03 -0.12 0.14 -0.13 0.08 0.24 0.03 0.03 0.05 -0.12 -0.13 0.62
pleasure 0.71 0.02 -0.05 0.18 -0.13 0.00 -0.10 0.07 -0.15 -0.03 0.05 -0.06 0.05 0.60
pride 0.76 0.32 -0.23 0.01 0.00 -0.01 0.01 -0.06 -0.03 -0.06 -0.06 0.08 0.08 0.77
reasoning 0.21 0.26 0.47 -0.07 -0.13 0.09 0.04 -0.04 0.00 -0.02 -0.01 -0.09 -0.06 0.38
recognizing 0.01 0.49 0.33 0.15 0.03 0.16 0.09 0.18 0.02 0.00 0.08 -0.10 0.08 0.46
remembering 0.01 0.54 0.35 0.12 -0.13 0.16 0.05 -0.03 0.03 0.18 -0.28 -0.07 -0.05 0.59
safe 0.68 -0.17 0.26 0.01 -0.11 -0.25 0.10 -0.01 -0.19 -0.15 -0.06 -0.04 -0.06 0.72
seeing 0.12 0.03 0.38 0.11 -0.04 0.03 0.06 -0.05 0.06 0.13 -0.01 0.07 0.08 0.22
self_aware 0.39 0.13 0.37 -0.17 -0.10 -0.20 0.23 -0.25 0.09 0.17 0.09 0.08 -0.01 0.55
self_restraint 0.44 0.27 0.13 -0.16 -0.06 -0.13 -0.11 0.18 0.21 0.05 0.05 0.09 0.09 0.44
sounds -0.02 0.03 0.42 0.17 0.00 0.08 0.16 -0.02 0.01 -0.04 0.14 0.10 0.05 0.27
temperature -0.20 0.05 0.53 0.35 0.14 -0.15 -0.24 -0.06 -0.05 0.04 -0.10 0.10 -0.04 0.57
thoughts 0.62 0.13 0.20 -0.14 -0.16 -0.10 -0.06 0.03 0.09 0.04 0.06 0.02 -0.03 0.51
tired 0.64 -0.29 0.11 0.16 0.03 0.00 0.06 0.06 0.19 0.02 -0.01 0.18 -0.02 0.61
u2 com
angry 0.37 1.4
beliefs 0.45 2.3
calm 0.47 1.1
choices 0.58 3.7
communicating 0.50 4.7
computations 0.17 1.5
conscious 0.54 2.4
depressed 0.31 1.3
depth 0.54 2.5
desires 0.32 2.2
disrespected 0.32 1.5
embarrassed 0.33 2.9
emo_recog 0.54 3.1
fear 0.29 1.9
free_will 0.37 4.3
goal 0.66 3.6
guilt 0.33 2.1
happy 0.21 1.4
hungry 0.16 2.0
intentions 0.56 3.0
joy 0.21 1.5
love 0.35 1.5
morality 0.46 3.8
nauseated 0.52 3.4
odors 0.40 3.5
pain 0.29 2.3
personality 0.38 3.0
pleasure 0.40 1.4
pride 0.23 1.7
reasoning 0.62 2.5
recognizing 0.54 3.0
remembering 0.41 3.2
safe 0.28 2.2
seeing 0.78 2.1
self_aware 0.45 5.8
self_restraint 0.56 4.1
sounds 0.73 2.2
temperature 0.43 3.2
thoughts 0.49 1.8
tired 0.39 2.1
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10 MR11 MR12 MR13
SS loadings 11.01 4.45 2.92 0.86 0.65 0.63 0.49 0.49 0.41 0.34 0.33 0.28 0.21
Proportion Var 0.28 0.11 0.07 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.28 0.39 0.46 0.48 0.50 0.51 0.53 0.54 0.55 0.56 0.56 0.57 0.58
Proportion Explained 0.48 0.19 0.13 0.04 0.03 0.03 0.02 0.02 0.02 0.01 0.01 0.01 0.01
Cumulative Proportion 0.48 0.67 0.80 0.83 0.86 0.89 0.91 0.93 0.95 0.96 0.98 0.99 1.00
Mean item complexity = 2.6
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 21.99 with Chi Square of 8462.22
The degrees of freedom for the model are 338 and the objective function was 1.02
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.02
The harmonic number of observations is 394 with the empirical chi square 160.41 with prob < 1
The total number of observations was 400 with MLE Chi Square = 382.35 with prob < 0.048
Tucker Lewis Index of factoring reliability = 0.986
RMSEA index = 0.022 and the 90 % confidence intervals are 0.002 0.027
BIC = -1642.77
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR5 MR6 MR7 MR8 MR9 MR10
Correlation of scores with factors 0.99 0.97 0.93 0.82 0.81 0.77 0.75 0.73 0.72 0.70
Multiple R square of scores with factors 0.97 0.94 0.87 0.67 0.65 0.60 0.56 0.53 0.52 0.49
Minimum correlation of possible factor scores 0.94 0.88 0.73 0.34 0.30 0.20 0.12 0.06 0.05 -0.02
MR11 MR12 MR13
Correlation of scores with factors 0.68 0.63 0.59
Multiple R square of scores with factors 0.46 0.39 0.35
Minimum correlation of possible factor scores -0.08 -0.21 -0.31# count factors with eigenvalues > 1 and variance explained > 5%
efa_comb_max_unrot_nfactors <- efa_comb_max_unrot_eigenvalues %>%
filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
count() %>%
as.numeric()
efa_comb_max_unrot_nfactors[1] 3# manually check that each factor is the dominant factor for at least one mental capacity item
efa_comb_max_unrot_loadings <- fa.sort(loadings(efa_comb_max_unrot)[]) %>%
data.frame() %>%
select(1:efa_comb_max_unrot_nfactors) %>%
rename(F1 = MR1, F2 = MR2, F3 = MR3) %>% # adjust by hand as needed
mutate(F1_abs = abs(F1),
F2_abs = abs(F2),
F3_abs = abs(F3),
loading_abs = pmax(F1_abs, F2_abs, F3_abs),
loading = ifelse(loading_abs == abs(F1), F1,
ifelse(loading_abs == abs(F2), F2,
ifelse(loading_abs == abs(F3), F3,
NA))),
factor = ifelse(loading == F1, "F1",
ifelse(loading == F2, "F2",
ifelse(loading == F3, "F3",
NA))),
factorName = ifelse(loading == F1, "Factor 1",
ifelse(loading == F2, "Factor 2",
ifelse(loading == F3, "Factor 3",
NA))))
efa_comb_max_unrot_loadings %>% count(factorName) # drop any factors where n < 1Maximal (13-factor) rotated solution
# do factor analysis
efa_comb_max_rot <- fa(d3_combined, nfactors = 13, rotate = rot_type, cor = cor_type)
efa_comb_max_rotFactor Analysis using method = minres
Call: fa(r = d3_combined, nfactors = 13, rotate = rot_type, cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR7 MR8 MR6 MR5 MR9 MR10 MR11 MR13 MR12 h2
angry 0.60 0.31 0.02 0.17 -0.16 0.08 0.10 0.21 -0.05 0.12 -0.02 0.20 0.03 0.63
beliefs 0.57 -0.03 0.05 0.18 0.00 0.10 0.31 0.13 0.23 0.03 0.01 -0.08 0.04 0.55
calm 0.58 0.34 0.02 0.11 -0.04 0.13 0.08 -0.07 0.07 0.13 -0.07 0.04 0.11 0.53
choices 0.14 0.10 0.17 0.57 0.06 0.14 0.10 0.02 0.05 -0.05 0.00 0.00 0.06 0.42
communicating 0.11 -0.12 0.67 0.09 0.02 0.07 -0.02 0.04 -0.06 0.00 0.03 0.00 -0.01 0.50
computations 0.03 -0.73 0.38 0.08 0.24 -0.08 0.17 0.09 0.02 -0.01 -0.07 0.03 0.22 0.83
conscious 0.08 0.18 0.16 0.22 0.29 0.49 -0.06 0.02 -0.05 0.05 -0.10 -0.08 -0.03 0.46
depressed 0.75 0.25 0.07 0.10 -0.14 0.03 0.08 0.00 -0.08 0.02 0.06 0.04 0.12 0.69
depth -0.12 0.02 0.18 0.15 0.60 0.08 0.03 0.09 0.01 0.00 -0.08 0.01 -0.11 0.46
desires 0.48 0.39 0.04 0.16 -0.10 0.09 -0.02 0.07 0.50 -0.01 0.02 -0.02 -0.05 0.68
disrespected 0.75 0.14 -0.02 0.12 -0.09 0.13 0.12 0.09 -0.05 -0.13 0.01 0.00 -0.08 0.68
embarrassed 0.62 0.07 0.02 0.02 -0.08 0.01 0.05 0.51 0.04 -0.05 -0.01 -0.01 0.02 0.67
emo_recog 0.45 -0.16 0.17 0.00 0.09 0.22 0.36 0.06 -0.01 0.05 0.09 -0.08 -0.02 0.46
fear 0.43 0.64 0.08 0.05 -0.08 0.09 -0.01 0.02 0.04 0.28 0.02 0.09 -0.04 0.71
free_will 0.13 0.10 0.09 0.76 0.09 0.08 0.00 0.01 0.03 0.07 0.00 0.01 -0.06 0.63
goal 0.33 0.02 0.17 0.18 0.02 0.15 0.20 -0.01 0.26 0.13 0.00 0.11 0.08 0.34
guilt 0.71 0.06 -0.03 0.05 0.07 0.00 0.07 0.32 0.11 0.02 -0.04 0.09 -0.13 0.67
happy 0.85 0.16 0.08 0.10 -0.10 0.08 -0.01 -0.08 -0.01 0.09 0.02 -0.03 0.04 0.79
hungry 0.16 0.85 -0.27 0.09 0.05 0.05 -0.10 0.01 0.03 0.00 -0.01 -0.05 0.01 0.84
intentions 0.24 -0.23 0.22 0.33 0.12 0.14 0.26 0.03 0.09 -0.05 0.03 0.24 -0.01 0.44
joy 0.84 0.18 0.08 0.05 -0.11 0.00 0.02 -0.11 0.08 -0.05 -0.04 -0.07 0.01 0.79
love 0.76 0.15 0.00 0.05 0.04 0.07 0.09 0.01 0.03 -0.09 0.14 -0.06 -0.02 0.65
morality 0.47 -0.11 0.16 0.06 0.09 -0.01 0.50 -0.03 -0.01 0.08 0.06 0.06 -0.11 0.54
nauseated 0.20 0.63 0.03 0.02 0.09 -0.06 0.01 0.06 0.08 -0.01 0.07 -0.02 0.10 0.48
odors -0.06 0.64 0.01 0.08 0.16 0.13 -0.03 0.02 -0.02 -0.02 -0.35 -0.08 -0.08 0.60
pain 0.25 0.78 -0.10 0.12 -0.02 0.07 0.02 -0.03 0.01 -0.03 -0.04 0.02 -0.01 0.71
personality 0.53 0.01 0.13 0.16 -0.06 0.06 0.46 0.20 0.09 0.05 -0.03 -0.13 0.09 0.62
pleasure 0.66 0.29 0.05 0.03 0.01 -0.02 0.05 -0.18 0.09 0.13 -0.05 -0.06 -0.04 0.60
pride 0.83 0.03 0.02 0.05 -0.10 0.04 0.09 0.08 0.06 0.09 0.04 0.15 -0.02 0.77
reasoning 0.11 0.00 0.38 0.37 0.12 0.23 0.08 -0.03 0.00 0.09 0.04 -0.07 0.00 0.38
recognizing 0.07 -0.20 0.56 0.12 0.17 -0.03 0.22 0.01 0.08 -0.02 -0.10 -0.01 0.00 0.46
remembering 0.09 -0.27 0.54 0.18 0.17 0.08 0.11 -0.02 0.02 -0.01 0.37 -0.03 -0.05 0.59
safe 0.43 0.46 -0.03 0.16 0.08 0.27 0.13 -0.07 0.07 0.43 -0.01 -0.05 0.00 0.72
seeing 0.01 0.15 0.27 0.08 0.20 0.23 0.04 -0.08 0.06 -0.06 0.06 0.09 -0.01 0.22
self_aware 0.23 0.10 0.13 0.19 0.02 0.64 0.14 0.03 0.07 0.02 0.04 0.05 0.02 0.55
self_restraint 0.38 0.04 0.07 0.26 -0.02 0.12 0.38 -0.09 -0.04 -0.11 -0.03 0.15 0.09 0.44
sounds -0.12 0.10 0.35 0.03 0.21 0.19 -0.04 -0.02 0.07 0.04 -0.14 0.08 0.09 0.27
temperature -0.20 0.00 0.16 0.03 0.68 0.08 0.02 -0.11 -0.05 0.01 0.10 -0.01 0.11 0.57
thoughts 0.48 0.21 0.07 0.30 -0.04 0.26 0.22 -0.11 0.00 0.01 0.01 0.00 0.07 0.51
tired 0.40 0.62 0.04 -0.02 0.02 0.13 0.05 0.01 -0.01 -0.03 0.03 0.14 0.16 0.61
u2 com
angry 0.37 2.7
beliefs 0.45 2.5
calm 0.47 2.3
choices 0.58 1.7
communicating 0.50 1.2
computations 0.17 2.2
conscious 0.54 3.2
depressed 0.31 1.5
depth 0.54 1.7
desires 0.32 3.4
disrespected 0.32 1.4
embarrassed 0.33 2.0
emo_recog 0.54 3.5
fear 0.29 2.4
free_will 0.37 1.2
goal 0.66 5.4
guilt 0.33 1.7
happy 0.21 1.2
hungry 0.16 1.4
intentions 0.56 6.7
joy 0.21 1.2
love 0.35 1.3
morality 0.46 2.7
nauseated 0.52 1.4
odors 0.40 1.9
pain 0.29 1.3
personality 0.38 3.0
pleasure 0.40 1.8
pride 0.23 1.2
reasoning 0.62 3.5
recognizing 0.54 2.1
remembering 0.41 3.2
safe 0.28 4.3
seeing 0.78 4.7
self_aware 0.45 1.8
self_restraint 0.56 3.9
sounds 0.73 3.8
temperature 0.43 1.5
thoughts 0.49 3.6
tired 0.39 2.2
MR1 MR2 MR3 MR4 MR7 MR8 MR6 MR5 MR9 MR10 MR11 MR13 MR12
SS loadings 8.54 4.66 1.95 1.74 1.33 1.21 1.21 0.62 0.49 0.42 0.38 0.29 0.24
Proportion Var 0.21 0.12 0.05 0.04 0.03 0.03 0.03 0.02 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.21 0.33 0.38 0.42 0.46 0.49 0.52 0.53 0.54 0.55 0.56 0.57 0.58
Proportion Explained 0.37 0.20 0.08 0.08 0.06 0.05 0.05 0.03 0.02 0.02 0.02 0.01 0.01
Cumulative Proportion 0.37 0.57 0.66 0.73 0.79 0.84 0.89 0.92 0.94 0.96 0.98 0.99 1.00
Mean item complexity = 2.5
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 21.99 with Chi Square of 8462.22
The degrees of freedom for the model are 338 and the objective function was 1.02
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.02
The harmonic number of observations is 394 with the empirical chi square 160.41 with prob < 1
The total number of observations was 400 with MLE Chi Square = 382.35 with prob < 0.048
Tucker Lewis Index of factoring reliability = 0.986
RMSEA index = 0.022 and the 90 % confidence intervals are 0.002 0.027
BIC = -1642.77
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR7 MR8 MR6 MR5 MR9 MR10
Correlation of scores with factors 0.96 0.95 0.84 0.83 0.83 0.77 0.77 0.78 0.72 0.72
Multiple R square of scores with factors 0.93 0.90 0.70 0.70 0.68 0.60 0.59 0.61 0.51 0.52
Minimum correlation of possible factor scores 0.85 0.80 0.41 0.39 0.36 0.20 0.18 0.22 0.03 0.05
MR11 MR13 MR12
Correlation of scores with factors 0.68 0.63 0.63
Multiple R square of scores with factors 0.46 0.40 0.40
Minimum correlation of possible factor scores -0.07 -0.21 -0.21# examine eigenvalues and variance explained
efa_comb_max_rot_eigenvalues <- print(efa_comb_max_rot)$Vaccounted %>%
t() %>%
data.frame()Factor Analysis using method = minres
Call: fa(r = d3_combined, nfactors = 13, rotate = rot_type, cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 MR4 MR7 MR8 MR6 MR5 MR9 MR10 MR11 MR13 MR12 h2
angry 0.60 0.31 0.02 0.17 -0.16 0.08 0.10 0.21 -0.05 0.12 -0.02 0.20 0.03 0.63
beliefs 0.57 -0.03 0.05 0.18 0.00 0.10 0.31 0.13 0.23 0.03 0.01 -0.08 0.04 0.55
calm 0.58 0.34 0.02 0.11 -0.04 0.13 0.08 -0.07 0.07 0.13 -0.07 0.04 0.11 0.53
choices 0.14 0.10 0.17 0.57 0.06 0.14 0.10 0.02 0.05 -0.05 0.00 0.00 0.06 0.42
communicating 0.11 -0.12 0.67 0.09 0.02 0.07 -0.02 0.04 -0.06 0.00 0.03 0.00 -0.01 0.50
computations 0.03 -0.73 0.38 0.08 0.24 -0.08 0.17 0.09 0.02 -0.01 -0.07 0.03 0.22 0.83
conscious 0.08 0.18 0.16 0.22 0.29 0.49 -0.06 0.02 -0.05 0.05 -0.10 -0.08 -0.03 0.46
depressed 0.75 0.25 0.07 0.10 -0.14 0.03 0.08 0.00 -0.08 0.02 0.06 0.04 0.12 0.69
depth -0.12 0.02 0.18 0.15 0.60 0.08 0.03 0.09 0.01 0.00 -0.08 0.01 -0.11 0.46
desires 0.48 0.39 0.04 0.16 -0.10 0.09 -0.02 0.07 0.50 -0.01 0.02 -0.02 -0.05 0.68
disrespected 0.75 0.14 -0.02 0.12 -0.09 0.13 0.12 0.09 -0.05 -0.13 0.01 0.00 -0.08 0.68
embarrassed 0.62 0.07 0.02 0.02 -0.08 0.01 0.05 0.51 0.04 -0.05 -0.01 -0.01 0.02 0.67
emo_recog 0.45 -0.16 0.17 0.00 0.09 0.22 0.36 0.06 -0.01 0.05 0.09 -0.08 -0.02 0.46
fear 0.43 0.64 0.08 0.05 -0.08 0.09 -0.01 0.02 0.04 0.28 0.02 0.09 -0.04 0.71
free_will 0.13 0.10 0.09 0.76 0.09 0.08 0.00 0.01 0.03 0.07 0.00 0.01 -0.06 0.63
goal 0.33 0.02 0.17 0.18 0.02 0.15 0.20 -0.01 0.26 0.13 0.00 0.11 0.08 0.34
guilt 0.71 0.06 -0.03 0.05 0.07 0.00 0.07 0.32 0.11 0.02 -0.04 0.09 -0.13 0.67
happy 0.85 0.16 0.08 0.10 -0.10 0.08 -0.01 -0.08 -0.01 0.09 0.02 -0.03 0.04 0.79
hungry 0.16 0.85 -0.27 0.09 0.05 0.05 -0.10 0.01 0.03 0.00 -0.01 -0.05 0.01 0.84
intentions 0.24 -0.23 0.22 0.33 0.12 0.14 0.26 0.03 0.09 -0.05 0.03 0.24 -0.01 0.44
joy 0.84 0.18 0.08 0.05 -0.11 0.00 0.02 -0.11 0.08 -0.05 -0.04 -0.07 0.01 0.79
love 0.76 0.15 0.00 0.05 0.04 0.07 0.09 0.01 0.03 -0.09 0.14 -0.06 -0.02 0.65
morality 0.47 -0.11 0.16 0.06 0.09 -0.01 0.50 -0.03 -0.01 0.08 0.06 0.06 -0.11 0.54
nauseated 0.20 0.63 0.03 0.02 0.09 -0.06 0.01 0.06 0.08 -0.01 0.07 -0.02 0.10 0.48
odors -0.06 0.64 0.01 0.08 0.16 0.13 -0.03 0.02 -0.02 -0.02 -0.35 -0.08 -0.08 0.60
pain 0.25 0.78 -0.10 0.12 -0.02 0.07 0.02 -0.03 0.01 -0.03 -0.04 0.02 -0.01 0.71
personality 0.53 0.01 0.13 0.16 -0.06 0.06 0.46 0.20 0.09 0.05 -0.03 -0.13 0.09 0.62
pleasure 0.66 0.29 0.05 0.03 0.01 -0.02 0.05 -0.18 0.09 0.13 -0.05 -0.06 -0.04 0.60
pride 0.83 0.03 0.02 0.05 -0.10 0.04 0.09 0.08 0.06 0.09 0.04 0.15 -0.02 0.77
reasoning 0.11 0.00 0.38 0.37 0.12 0.23 0.08 -0.03 0.00 0.09 0.04 -0.07 0.00 0.38
recognizing 0.07 -0.20 0.56 0.12 0.17 -0.03 0.22 0.01 0.08 -0.02 -0.10 -0.01 0.00 0.46
remembering 0.09 -0.27 0.54 0.18 0.17 0.08 0.11 -0.02 0.02 -0.01 0.37 -0.03 -0.05 0.59
safe 0.43 0.46 -0.03 0.16 0.08 0.27 0.13 -0.07 0.07 0.43 -0.01 -0.05 0.00 0.72
seeing 0.01 0.15 0.27 0.08 0.20 0.23 0.04 -0.08 0.06 -0.06 0.06 0.09 -0.01 0.22
self_aware 0.23 0.10 0.13 0.19 0.02 0.64 0.14 0.03 0.07 0.02 0.04 0.05 0.02 0.55
self_restraint 0.38 0.04 0.07 0.26 -0.02 0.12 0.38 -0.09 -0.04 -0.11 -0.03 0.15 0.09 0.44
sounds -0.12 0.10 0.35 0.03 0.21 0.19 -0.04 -0.02 0.07 0.04 -0.14 0.08 0.09 0.27
temperature -0.20 0.00 0.16 0.03 0.68 0.08 0.02 -0.11 -0.05 0.01 0.10 -0.01 0.11 0.57
thoughts 0.48 0.21 0.07 0.30 -0.04 0.26 0.22 -0.11 0.00 0.01 0.01 0.00 0.07 0.51
tired 0.40 0.62 0.04 -0.02 0.02 0.13 0.05 0.01 -0.01 -0.03 0.03 0.14 0.16 0.61
u2 com
angry 0.37 2.7
beliefs 0.45 2.5
calm 0.47 2.3
choices 0.58 1.7
communicating 0.50 1.2
computations 0.17 2.2
conscious 0.54 3.2
depressed 0.31 1.5
depth 0.54 1.7
desires 0.32 3.4
disrespected 0.32 1.4
embarrassed 0.33 2.0
emo_recog 0.54 3.5
fear 0.29 2.4
free_will 0.37 1.2
goal 0.66 5.4
guilt 0.33 1.7
happy 0.21 1.2
hungry 0.16 1.4
intentions 0.56 6.7
joy 0.21 1.2
love 0.35 1.3
morality 0.46 2.7
nauseated 0.52 1.4
odors 0.40 1.9
pain 0.29 1.3
personality 0.38 3.0
pleasure 0.40 1.8
pride 0.23 1.2
reasoning 0.62 3.5
recognizing 0.54 2.1
remembering 0.41 3.2
safe 0.28 4.3
seeing 0.78 4.7
self_aware 0.45 1.8
self_restraint 0.56 3.9
sounds 0.73 3.8
temperature 0.43 1.5
thoughts 0.49 3.6
tired 0.39 2.2
MR1 MR2 MR3 MR4 MR7 MR8 MR6 MR5 MR9 MR10 MR11 MR13 MR12
SS loadings 8.54 4.66 1.95 1.74 1.33 1.21 1.21 0.62 0.49 0.42 0.38 0.29 0.24
Proportion Var 0.21 0.12 0.05 0.04 0.03 0.03 0.03 0.02 0.01 0.01 0.01 0.01 0.01
Cumulative Var 0.21 0.33 0.38 0.42 0.46 0.49 0.52 0.53 0.54 0.55 0.56 0.57 0.58
Proportion Explained 0.37 0.20 0.08 0.08 0.06 0.05 0.05 0.03 0.02 0.02 0.02 0.01 0.01
Cumulative Proportion 0.37 0.57 0.66 0.73 0.79 0.84 0.89 0.92 0.94 0.96 0.98 0.99 1.00
Mean item complexity = 2.5
Test of the hypothesis that 13 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 21.99 with Chi Square of 8462.22
The degrees of freedom for the model are 338 and the objective function was 1.02
The root mean square of the residuals (RMSR) is 0.02
The df corrected root mean square of the residuals is 0.02
The harmonic number of observations is 394 with the empirical chi square 160.41 with prob < 1
The total number of observations was 400 with MLE Chi Square = 382.35 with prob < 0.048
Tucker Lewis Index of factoring reliability = 0.986
RMSEA index = 0.022 and the 90 % confidence intervals are 0.002 0.027
BIC = -1642.77
Fit based upon off diagonal values = 1
Measures of factor score adequacy
MR1 MR2 MR3 MR4 MR7 MR8 MR6 MR5 MR9 MR10
Correlation of scores with factors 0.96 0.95 0.84 0.83 0.83 0.77 0.77 0.78 0.72 0.72
Multiple R square of scores with factors 0.93 0.90 0.70 0.70 0.68 0.60 0.59 0.61 0.51 0.52
Minimum correlation of possible factor scores 0.85 0.80 0.41 0.39 0.36 0.20 0.18 0.22 0.03 0.05
MR11 MR13 MR12
Correlation of scores with factors 0.68 0.63 0.63
Multiple R square of scores with factors 0.46 0.40 0.40
Minimum correlation of possible factor scores -0.07 -0.21 -0.21efa_comb_max_rot_eigenvalues# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"Small rotated solution
# do factor analysis
efa_comb_small_rot <- fa(d3_combined, nfactors = efa_comb_max_unrot_nfactors, rotate = rot_type, cor = cor_type)
efa_comb_small_rotFactor Analysis using method = minres
Call: fa(r = d3_combined, nfactors = efa_comb_max_unrot_nfactors, rotate = rot_type,
cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 h2 u2 com
angry 0.67 0.30 0.04 0.53 0.47 1.4
beliefs 0.65 -0.04 0.18 0.46 0.54 1.2
calm 0.60 0.35 0.11 0.50 0.50 1.7
choices 0.23 0.09 0.46 0.27 0.73 1.6
communicating 0.17 -0.26 0.40 0.26 0.74 2.1
computations 0.05 -0.79 0.31 0.73 0.27 1.3
conscious 0.08 0.23 0.51 0.31 0.69 1.4
depressed 0.77 0.23 0.00 0.65 0.35 1.2
depth -0.16 0.00 0.49 0.27 0.73 1.2
desires 0.53 0.39 0.09 0.44 0.56 1.9
disrespected 0.77 0.15 0.00 0.62 0.38 1.1
embarrassed 0.65 0.05 -0.06 0.42 0.58 1.0
emo_recog 0.51 -0.21 0.24 0.36 0.64 1.8
fear 0.46 0.62 0.10 0.60 0.40 1.9
free_will 0.20 0.14 0.45 0.26 0.74 1.6
goal 0.42 -0.01 0.29 0.26 0.74 1.8
guilt 0.71 0.07 0.00 0.51 0.49 1.0
happy 0.83 0.18 0.02 0.73 0.27 1.1
hungry 0.13 0.90 -0.04 0.82 0.18 1.0
intentions 0.33 -0.27 0.40 0.35 0.65 2.7
joy 0.82 0.18 -0.03 0.71 0.29 1.1
love 0.74 0.16 0.03 0.58 0.42 1.1
morality 0.54 -0.19 0.22 0.37 0.63 1.6
nauseated 0.21 0.58 0.09 0.39 0.61 1.3
odors -0.08 0.64 0.22 0.46 0.54 1.3
pain 0.27 0.79 0.06 0.69 0.31 1.2
personality 0.64 -0.06 0.20 0.46 0.54 1.2
pleasure 0.64 0.30 0.04 0.50 0.50 1.4
pride 0.86 0.03 -0.05 0.74 0.26 1.0
reasoning 0.18 -0.04 0.55 0.34 0.66 1.2
recognizing 0.14 -0.34 0.46 0.35 0.65 2.0
remembering 0.16 -0.39 0.47 0.40 0.60 2.2
safe 0.47 0.50 0.25 0.54 0.46 2.5
seeing 0.03 0.11 0.40 0.18 0.82 1.2
self_aware 0.31 0.13 0.42 0.29 0.71 2.1
self_restraint 0.47 0.00 0.25 0.28 0.72 1.5
sounds -0.11 0.05 0.42 0.19 0.81 1.2
temperature -0.26 -0.03 0.46 0.28 0.72 1.6
thoughts 0.55 0.22 0.29 0.44 0.56 1.9
tired 0.41 0.58 0.11 0.51 0.49 1.9
MR1 MR2 MR3
SS loadings 9.62 5.04 3.39
Proportion Var 0.24 0.13 0.08
Cumulative Var 0.24 0.37 0.45
Proportion Explained 0.53 0.28 0.19
Cumulative Proportion 0.53 0.81 1.00
Mean item complexity = 1.5
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 21.99 with Chi Square of 8462.22
The degrees of freedom for the model are 663 and the objective function was 3.26
The root mean square of the residuals (RMSR) is 0.04
The df corrected root mean square of the residuals is 0.04
The harmonic number of observations is 394 with the empirical chi square 865.63 with prob < 1.7e-07
The total number of observations was 400 with MLE Chi Square = 1248.87 with prob < 2.2e-38
Tucker Lewis Index of factoring reliability = 0.91
RMSEA index = 0.049 and the 90 % confidence intervals are 0.043 0.051
BIC = -2723.48
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy
MR1 MR2 MR3
Correlation of scores with factors 0.97 0.96 0.91
Multiple R square of scores with factors 0.95 0.93 0.83
Minimum correlation of possible factor scores 0.90 0.85 0.65# examine eigenvalues and variance explained
efa_comb_small_rot_eigenvalues <- print(efa_comb_small_rot)$Vaccounted %>%
t() %>%
data.frame()Factor Analysis using method = minres
Call: fa(r = d3_combined, nfactors = efa_comb_max_unrot_nfactors, rotate = rot_type,
cor = cor_type)
Standardized loadings (pattern matrix) based upon correlation matrix
MR1 MR2 MR3 h2 u2 com
angry 0.67 0.30 0.04 0.53 0.47 1.4
beliefs 0.65 -0.04 0.18 0.46 0.54 1.2
calm 0.60 0.35 0.11 0.50 0.50 1.7
choices 0.23 0.09 0.46 0.27 0.73 1.6
communicating 0.17 -0.26 0.40 0.26 0.74 2.1
computations 0.05 -0.79 0.31 0.73 0.27 1.3
conscious 0.08 0.23 0.51 0.31 0.69 1.4
depressed 0.77 0.23 0.00 0.65 0.35 1.2
depth -0.16 0.00 0.49 0.27 0.73 1.2
desires 0.53 0.39 0.09 0.44 0.56 1.9
disrespected 0.77 0.15 0.00 0.62 0.38 1.1
embarrassed 0.65 0.05 -0.06 0.42 0.58 1.0
emo_recog 0.51 -0.21 0.24 0.36 0.64 1.8
fear 0.46 0.62 0.10 0.60 0.40 1.9
free_will 0.20 0.14 0.45 0.26 0.74 1.6
goal 0.42 -0.01 0.29 0.26 0.74 1.8
guilt 0.71 0.07 0.00 0.51 0.49 1.0
happy 0.83 0.18 0.02 0.73 0.27 1.1
hungry 0.13 0.90 -0.04 0.82 0.18 1.0
intentions 0.33 -0.27 0.40 0.35 0.65 2.7
joy 0.82 0.18 -0.03 0.71 0.29 1.1
love 0.74 0.16 0.03 0.58 0.42 1.1
morality 0.54 -0.19 0.22 0.37 0.63 1.6
nauseated 0.21 0.58 0.09 0.39 0.61 1.3
odors -0.08 0.64 0.22 0.46 0.54 1.3
pain 0.27 0.79 0.06 0.69 0.31 1.2
personality 0.64 -0.06 0.20 0.46 0.54 1.2
pleasure 0.64 0.30 0.04 0.50 0.50 1.4
pride 0.86 0.03 -0.05 0.74 0.26 1.0
reasoning 0.18 -0.04 0.55 0.34 0.66 1.2
recognizing 0.14 -0.34 0.46 0.35 0.65 2.0
remembering 0.16 -0.39 0.47 0.40 0.60 2.2
safe 0.47 0.50 0.25 0.54 0.46 2.5
seeing 0.03 0.11 0.40 0.18 0.82 1.2
self_aware 0.31 0.13 0.42 0.29 0.71 2.1
self_restraint 0.47 0.00 0.25 0.28 0.72 1.5
sounds -0.11 0.05 0.42 0.19 0.81 1.2
temperature -0.26 -0.03 0.46 0.28 0.72 1.6
thoughts 0.55 0.22 0.29 0.44 0.56 1.9
tired 0.41 0.58 0.11 0.51 0.49 1.9
MR1 MR2 MR3
SS loadings 9.62 5.04 3.39
Proportion Var 0.24 0.13 0.08
Cumulative Var 0.24 0.37 0.45
Proportion Explained 0.53 0.28 0.19
Cumulative Proportion 0.53 0.81 1.00
Mean item complexity = 1.5
Test of the hypothesis that 3 factors are sufficient.
The degrees of freedom for the null model are 780 and the objective function was 21.99 with Chi Square of 8462.22
The degrees of freedom for the model are 663 and the objective function was 3.26
The root mean square of the residuals (RMSR) is 0.04
The df corrected root mean square of the residuals is 0.04
The harmonic number of observations is 394 with the empirical chi square 865.63 with prob < 1.7e-07
The total number of observations was 400 with MLE Chi Square = 1248.87 with prob < 2.2e-38
Tucker Lewis Index of factoring reliability = 0.91
RMSEA index = 0.049 and the 90 % confidence intervals are 0.043 0.051
BIC = -2723.48
Fit based upon off diagonal values = 0.98
Measures of factor score adequacy
MR1 MR2 MR3
Correlation of scores with factors 0.97 0.96 0.91
Multiple R square of scores with factors 0.95 0.93 0.83
Minimum correlation of possible factor scores 0.90 0.85 0.65efa_comb_small_rot_eigenvalues# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"Loadings
# make dataframe for all factor loadings and dominant factor
efa_comb_small_rot_loadings <- efa_comb_small_rot$loadings[] %>%
fa.sort() %>%
data.frame() %>%
rownames_to_column(var = "capacity") %>%
rename(F1 = MR1, F2 = MR2, F3 = MR3) %>% # adjust by hand as needed
mutate(F1_abs = abs(F1),
F2_abs = abs(F2),
F3_abs = abs(F3),
loading_abs = pmax(F1_abs, F2_abs, F3_abs),
loading = ifelse(loading_abs == abs(F1), F1,
ifelse(loading_abs == abs(F2), F2,
ifelse(loading_abs == abs(F3), F3,
NA))),
factor = ifelse(loading == F1, "F1",
ifelse(loading == F2, "F2",
ifelse(loading == F3, "F3",
NA))),
factorName = ifelse(loading == F1, "Factor 1",
ifelse(loading == F2, "Factor 2",
ifelse(loading == F3, "Factor 3",
NA)))) %>%
select(capacity, F1, F2, F3, factor, factorName, loading, loading_abs) %>%
distinct() %>%
full_join(d %>% select(capacity, capWording)) %>%
mutate(capWording = gsub("\\ ", "_", gsub(" --.*", "", capWording))) %>%
select(capacity, capWording, F1:loading_abs) %>%
distinct()Joining, by = "capacity"
joining factor and character vector, coercing into character vector# print out list of items by dominant factor
efa_comb_small_rot_items <- efa_comb_small_rot_loadings %>%
arrange(factor, desc(loading_abs)) %>%
mutate(capWordingPlus = ifelse(loading > 0, capWording,
paste0(capWording, " (negative loading)"))) %>%
group_by(factor) %>%
summarise(items = gsub("_", " ", paste(capWordingPlus, collapse = ", ")))
head(efa_comb_small_rot_items$items, 40)[1] "feel proud, feel happy, feel joy, feel sad, get hurt feelings, feel love, feel guilty, get angry, have beliefs, feel embarrassed, have a personality, feel pleasure, feel calm, have thoughts, know what's nice and what's mean, have desires, understand how somebody else is feeling, have self-control, have goals"
[2] "get hungry, do math (negative loading), feel pain, smell things, feel scared, feel sick, feel tired, feel safe"
[3] "figure out how to do things, be aware of things, sense whether something is close by or far away, remember things, sense temperatures, make choices, recognize somebody else, decide what to do, be aware of itself, hear sounds, see things, communicate with somebody else, make plans" Regression on factor scores
Children vs. adults
scores_all <- fa(d3_combined, nfactors = efa_comb_max_unrot_nfactors, rotate = rot_type,
cor = cor_type, scores = score_type)$scores %>%
data.frame() %>%
rownames_to_column(var = "subid") %>%
mutate(ageGroup = factor(ifelse(grepl("run", subid), "adult", "child")))
colnames(scores_all)[2:4] <- c("score_F1", "score_F2", "score_F3")# analyze
scores_all_analysis <- d %>%
select(subid, ageGroup, character) %>%
distinct() %>%
left_join(scores_all) %>%
mutate(character = factor(character)) %>%
filter(!is.na(score_F1) & !is.na(score_F2) & !is.na(score_F3), !is.na(ageGroup)) %>%
gather(factor, score, starts_with("score_")) %>%
mutate(factor = factor(factor))Joining, by = c("subid", "ageGroup")
joining character vector and factor, coercing into character vector# set contrasts
contrasts(scores_all_analysis$factor) <- cbind(F1 = c(1, -1, 0), # MAKE SURE TO DOUBLE-CHECK!!
F3 = c(0, -1, 1))
contrasts(scores_all_analysis$character) <- cbind(robot = c(-1, 1))
contrasts(scores_all_analysis$ageGroup) <- cbind(child = c(-1, 1))
r1 <- lmer(score ~ character * factor + (1 | subid), scores_all_analysis)
r2 <- lmer(score ~ character * factor + ageGroup + (1 | subid), scores_all_analysis)
r3 <- lmer(score ~ character * factor * ageGroup + (1 | subid), scores_all_analysis)
anova(r1, r2, r3)refitting model(s) with ML (instead of REML)Data: scores_all_analysis
Models:
r1: score ~ character * factor + (1 | subid)
r2: score ~ character * factor + ageGroup + (1 | subid)
r3: score ~ character * factor * ageGroup + (1 | subid)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
r1 8 2830.1 2870.3 -1407.0 2814.1
r2 9 2777.7 2823.0 -1379.9 2759.7 54.374 1 1.658e-13 ***
r3 14 2575.6 2646.0 -1273.8 2547.6 212.106 5 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# summary(r1)
# summary(r2)
summary(r3)Linear mixed model fit by REML ['lmerMod']
Formula: score ~ character * factor * ageGroup + (1 | subid)
Data: scores_all_analysis
REML criterion at convergence: 2612.3
Scaled residuals:
Min 1Q Median 3Q Max
-4.7304 -0.4705 0.0183 0.5793 2.9429
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.0639 0.2528
Residual 0.5125 0.7159
Number of obs: 1125, groups: subid, 375
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.006232 0.025038 -0.249
characterrobot -0.250911 0.025038 -10.021
factorF1 -0.001663 0.030207 -0.055
factorF3 0.012368 0.030207 0.409
ageGroupchild 0.191460 0.025038 7.647
characterrobot:factorF1 0.260869 0.030207 8.636
characterrobot:factorF3 0.391390 0.030207 12.957
characterrobot:ageGroupchild 0.046804 0.025038 1.869
factorF1:ageGroupchild 0.456654 0.030207 15.117
factorF3:ageGroupchild -0.298395 0.030207 -9.878
characterrobot:factorF1:ageGroupchild 0.003589 0.030207 0.119
characterrobot:factorF3:ageGroupchild -0.040662 0.030207 -1.346
Correlation of Fixed Effects:
(Intr) chrctr fctrF1 fctrF3 agGrpc chr:F1 chr:F3 chrc:G fcF1:G fcF3:G c:F1:G
charactrrbt 0.001
factorF1 0.000 0.000
factorF3 0.000 0.000 -0.500
ageGropchld -0.019 0.035 0.000 0.000
chrctrrb:F1 0.000 0.000 0.001 -0.001 0.000
chrctrrb:F3 0.000 0.000 -0.001 0.001 0.000 -0.500
chrctrrbt:G 0.035 -0.019 0.000 0.000 0.001 0.000 0.000
fctrF1:gGrp 0.000 0.000 -0.019 0.009 0.000 0.035 -0.017 0.000
fctrF3:gGrp 0.000 0.000 0.009 -0.019 0.000 -0.017 0.035 0.000 -0.500
chrctr:F1:G 0.000 0.000 0.035 -0.017 0.000 -0.019 0.009 0.000 0.001 -0.001
chrctr:F3:G 0.000 0.000 -0.017 0.035 0.000 0.009 -0.019 0.000 -0.001 0.001 -0.500round(signif(summary(r3)$coefficients, 3), 2) %>% data.frame() # %>% View()# stepwise regression
drop1(r3, test = "Chisq")Single term deletions
Model:
score ~ character * factor * ageGroup + (1 | subid)
Df AIC LRT Pr(Chi)
<none> 2575.6
character:factor:ageGroup 2 2573.9 2.2422 0.3259r3_step2 <- lmer(score ~ character * factor + ageGroup + character:ageGroup + factor:ageGroup + (1 | subid), scores_all_analysis)
drop1(r3_step2, test = "Chisq")Single term deletions
Model:
score ~ character * factor + ageGroup + character:ageGroup +
factor:ageGroup + (1 | subid)
Df AIC LRT Pr(Chi)
<none> 2573.9
character:factor 2 2941.8 371.91 <2e-16 ***
character:ageGroup 1 2575.4 3.52 0.0608 .
factor:ageGroup 2 2776.2 206.35 <2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1r3_step3 <- lmer(score ~ character * factor + ageGroup + factor:ageGroup + (1 | subid), scores_all_analysis)
drop1(r3_step3, test = "Chisq")Single term deletions
Model:
score ~ character * factor + ageGroup + factor:ageGroup + (1 |
subid)
Df AIC LRT Pr(Chi)
<none> 2575.4
character:factor 2 2942.9 371.52 < 2.2e-16 ***
factor:ageGroup 2 2777.7 206.35 < 2.2e-16 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# # robot only
# robot_r1 <- lmer(score ~ factor + (1 | subid), scores_all_analysis %>% filter(character == "robot"))
# robot_r2 <- lmer(score ~ factor + ageGroup + (1 | subid), scores_all_analysis %>% filter(character == "robot"))
# robot_r3 <- lmer(score ~ factor * ageGroup + (1 | subid), scores_all_analysis %>% filter(character == "robot"))
# anova(robot_r1, robot_r2, robot_r3)
# # summary(robot_r1)
# # summary(robot_r2)
# summary(robot_r3)scores_all_plotting <- d %>%
select(subid, ageGroup, character) %>%
distinct() %>%
full_join(scores_all) %>%
mutate(character = factor(character)) %>%
filter(!is.na(score_F1), !is.na(score_F2), !is.na(score_F3), !is.na(ageGroup)) %>%
gather(factor, score, starts_with("score_")) %>%
mutate(factor = factor(factor)) %>%
multi_boot(column = "score",
summary_groups = c("ageGroup", "character", "factor"),
statistics_functions = c("mean", "ci_lower", "ci_upper"))Joining, by = c("subid", "ageGroup")
joining character vector and factor, coercing into character vector# plot
ggplot(scores_all_plotting %>%
ungroup() %>%
mutate(factor = factor(factor,
labels = c("Social-emotional",
"Physiological",
"Perceptual-cognitive")),
ageGroup = factor(ageGroup,
levels = c("adult", "child"),
labels = c("adults", "children"))),
# levels = c("child", "adult"),
# labels = c("children", "adults"))),
aes(x = ageGroup, y = mean, color = character, shape = character)) +
facet_wrap("factor", ncol = 3) +
theme_bw() +
theme(text = element_text(size = 28),
legend.position = "bottom") +
geom_point(size = 5, position = position_dodge(width = 0.4)) +
geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper),
width = 0.2, position = position_dodge(width = 0.4)) +
scale_shape_manual(values = c(19, 15)) +
labs(title = "Factor scores by age group",
# subtitle = "Adults (Study 1) vs. children (Study 2)\n",
x = "Age group",
y = "Mean factor score") # 1000 by 500
Children by age at test
scores_children <- fa(d3_child, nfactors = efa_comb_max_unrot_nfactors, rotate = rot_type,
cor = cor_type, scores = score_type)$scores %>%
data.frame() %>%
rownames_to_column(var = "subid") %>%
mutate(ageGroup = factor(ifelse(grepl("run", subid), "adult", "child")))
colnames(scores_children)[2:4] <- c("score_F1", "score_F2", "score_F3")# analyze
scores_children_analysis <- d %>%
select(subid, age, character) %>%
distinct() %>%
left_join(scores_children) %>%
mutate(character = factor(character)) %>%
filter(!is.na(score_F1) & !is.na(score_F2) & !is.na(score_F3), !is.na(ageGroup)) %>%
gather(factor, score, starts_with("score_")) %>%
mutate(factor = factor(factor))Joining, by = "subid"
joining character vector and factor, coercing into character vector# set contrasts
contrasts(scores_children_analysis$factor) <- cbind(F1 = c(1, -1, 0), # MAKE SURE TO DOUBLE-CHECK!!
F3 = c(0, -1, 1))
contrasts(scores_children_analysis$character) <- cbind(robot = c(-1, 1))
r4 <- lmer(score ~ character * factor + (1 | subid), scores_children_analysis)
r5 <- lmer(score ~ character * factor + scale(age) + (1 | subid), scores_children_analysis)
r6 <- lmer(score ~ character * factor * scale(age) + (1 | subid), scores_children_analysis)
anova(r4, r5, r6)refitting model(s) with ML (instead of REML)Data: scores_children_analysis
Models:
r4: score ~ character * factor + (1 | subid)
r5: score ~ character * factor + scale(age) + (1 | subid)
r6: score ~ character * factor * scale(age) + (1 | subid)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
r4 8 1467.0 1501.8 -725.51 1451.0
r5 9 1468.9 1508.0 -725.43 1450.9 0.1519 1 0.6967
r6 14 1437.0 1497.9 -704.51 1409.0 41.8487 5 6.321e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# summary(r4)
# summary(r5)
summary(r6)Linear mixed model fit by REML ['lmerMod']
Formula: score ~ character * factor * scale(age) + (1 | subid)
Data: scores_children_analysis
REML criterion at convergence: 1463.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.8784 -0.5649 0.0598 0.6355 2.2010
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.07345 0.2710
Residual 0.63301 0.7956
Number of obs: 573, groups: subid, 191
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.007491 0.038635 -0.194
characterrobot -0.229166 0.038635 -5.932
factorF1 0.004129 0.047057 0.088
factorF3 0.029476 0.047057 0.626
scale(age) -0.021522 0.038776 -0.555
characterrobot:factorF1 0.237645 0.047057 5.050
characterrobot:factorF3 0.409935 0.047057 8.711
characterrobot:scale(age) -0.077890 0.038776 -2.009
factorF1:scale(age) -0.255520 0.047229 -5.410
factorF3:scale(age) 0.249338 0.047229 5.279
characterrobot:factorF1:scale(age) -0.062275 0.047229 -1.319
characterrobot:factorF3:scale(age) 0.042963 0.047229 0.910
Correlation of Fixed Effects:
(Intr) chrctr fctrF1 fctrF3 scl(g) chr:F1 chr:F3 chr:() fF1:() fF3:() c:F1:(
charactrrbt 0.037
factorF1 0.000 0.000
factorF3 0.000 0.000 -0.500
scale(age) 0.004 0.030 0.000 0.000
chrctrrb:F1 0.000 0.000 0.037 -0.018 0.000
chrctrrb:F3 0.000 0.000 -0.018 0.037 0.000 -0.500
chrctrrb:() 0.030 0.004 0.000 0.000 0.083 0.000 0.000
fctrF1:sc() 0.000 0.000 0.004 -0.002 0.000 0.030 -0.015 0.000
fctrF3:sc() 0.000 0.000 -0.002 0.004 0.000 -0.015 0.030 0.000 -0.500
chrct:F1:() 0.000 0.000 0.030 -0.015 0.000 0.004 -0.002 0.000 0.083 -0.041
chrct:F3:() 0.000 0.000 -0.015 0.030 0.000 -0.002 0.004 0.000 -0.041 0.083 -0.500# stepwise regression
drop1(r6, test = "Chisq")Single term deletions
Model:
score ~ character * factor * scale(age) + (1 | subid)
Df AIC LRT Pr(Chi)
<none> 1437.0
character:factor:scale(age) 2 1434.9 1.8567 0.3952r6_step2 <- lmer(score ~ character * factor + scale(age) + character:scale(age) + factor:scale(age) + (1 | subid), scores_children_analysis)
drop1(r6_step2, test = "Chisq")convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce qSingle term deletions
Model:
score ~ character * factor + scale(age) + character:scale(age) +
factor:scale(age) + (1 | subid)
Df AIC LRT Pr(Chi)
<none> 1434.9
character:factor 2 1590.7 159.841 < 2.2e-16 ***
character:scale(age) 1 1437.0 4.078 0.04346 *
factor:scale(age) 2 1466.8 35.914 1.59e-08 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1summary(r6_step2)Linear mixed model fit by REML ['lmerMod']
Formula: score ~ character * factor + scale(age) + character:scale(age) +
factor:scale(age) + (1 | subid)
Data: scores_children_analysis
REML criterion at convergence: 1456.2
Scaled residuals:
Min 1Q Median 3Q Max
-3.8592 -0.5629 0.0700 0.6348 2.2600
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.07355 0.2712
Residual 0.63271 0.7954
Number of obs: 573, groups: subid, 191
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.007491 0.038635 -0.194
characterrobot -0.229166 0.038635 -5.932
factorF1 0.005977 0.047025 0.127
factorF3 0.028201 0.047025 0.600
scale(age) -0.021522 0.038776 -0.555
characterrobot:factorF1 0.237865 0.047046 5.056
characterrobot:factorF3 0.409783 0.047046 8.710
characterrobot:scale(age) -0.077890 0.038776 -2.009
factorF1:scale(age) -0.250358 0.047055 -5.320
factorF3:scale(age) 0.245777 0.047055 5.223
Correlation of Fixed Effects:
(Intr) chrctr fctrF1 fctrF3 scl(g) chr:F1 chr:F3 chr:() fF1:()
charactrrbt 0.037
factorF1 0.000 0.000
factorF3 0.000 0.000 -0.500
scale(age) 0.004 0.030 0.000 0.000
chrctrrb:F1 0.000 0.000 0.037 -0.018 0.000
chrctrrb:F3 0.000 0.000 -0.018 0.037 0.000 -0.500
chrctrrb:() 0.030 0.004 0.000 0.000 0.083 0.000 0.000
fctrF1:sc() 0.000 0.000 0.001 -0.001 0.000 0.030 -0.015 0.000
fctrF3:sc() 0.000 0.000 -0.001 0.001 0.000 -0.015 0.030 0.000 -0.500# explore polynoial effects of age
r6b <- lmer(score ~ character * factor * poly(age, 1) + (1 | subid), scores_children_analysis)
r7 <- lmer(score ~ character * factor * poly(age, 2) + (1 | subid), scores_children_analysis)
r8 <- lmer(score ~ character * factor * poly(age, 3) + (1 | subid), scores_children_analysis)
anova(r6b, r7, r8)refitting model(s) with ML (instead of REML)Data: scores_children_analysis
Models:
r6b: score ~ character * factor * poly(age, 1) + (1 | subid)
r7: score ~ character * factor * poly(age, 2) + (1 | subid)
r8: score ~ character * factor * poly(age, 3) + (1 | subid)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
r6b 14 1437.0 1497.9 -704.51 1409.0
r7 20 1444.9 1532.0 -702.47 1404.9 4.0793 6 0.6659
r8 26 1450.4 1563.5 -699.22 1398.4 6.5048 6 0.3691# summary(r6b)
# summary(r7)
summary(r8)Linear mixed model fit by REML ['lmerMod']
Formula: score ~ character * factor * poly(age, 3) + (1 | subid)
Data: scores_children_analysis
REML criterion at convergence: 1392.5
Scaled residuals:
Min 1Q Median 3Q Max
-3.8366 -0.5301 0.0687 0.5974 2.2357
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.0716 0.2676
Residual 0.6367 0.7979
Number of obs: 573, groups: subid, 191
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.005954 0.038635 -0.154
characterrobot -0.225939 0.038635 -5.848
factorF1 0.005033 0.047247 0.107
factorF3 0.028466 0.047247 0.602
poly(age, 3)1 -0.507317 0.929561 -0.546
poly(age, 3)2 1.517377 0.929738 1.632
poly(age, 3)3 -1.064327 0.929152 -1.145
characterrobot:factorF1 0.237458 0.047247 5.026
characterrobot:factorF3 0.411414 0.047247 8.708
characterrobot:poly(age, 3)1 -1.896880 0.929561 -2.041
characterrobot:poly(age, 3)2 0.739308 0.929738 0.795
characterrobot:poly(age, 3)3 -0.183101 0.929152 -0.197
factorF1:poly(age, 3)1 -5.922489 1.136765 -5.210
factorF3:poly(age, 3)1 5.920107 1.136765 5.208
factorF1:poly(age, 3)2 -0.160440 1.136983 -0.141
factorF3:poly(age, 3)2 0.696858 1.136983 0.613
factorF1:poly(age, 3)3 0.613124 1.136266 0.540
factorF3:poly(age, 3)3 -0.982179 1.136266 -0.864
characterrobot:factorF1:poly(age, 3)1 -1.431553 1.136765 -1.259
characterrobot:factorF3:poly(age, 3)1 0.969558 1.136765 0.853
characterrobot:factorF1:poly(age, 3)2 0.705745 1.136983 0.621
characterrobot:factorF3:poly(age, 3)2 -0.555744 1.136983 -0.489
characterrobot:factorF1:poly(age, 3)3 2.159919 1.136266 1.901
characterrobot:factorF3:poly(age, 3)3 -0.305138 1.136266 -0.269
Correlation matrix not shown by default, as p = 24 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itround(signif(summary(r6)$coefficients, 3), 2) %>% data.frame() # %>% View()# stepwise regression
drop1(r8, test = "Chisq")Single term deletions
Model:
score ~ character * factor * poly(age, 3) + (1 | subid)
Df AIC LRT Pr(Chi)
<none> 1450.4
character:factor:poly(age, 3) 6 1445.0 6.5986 0.3596r8_step2 <- lmer(score ~ character * factor * poly(age, 2) + poly(age, 3) + poly(age, 3):character + poly(age, 3):factor + (1 | subid), scores_children_analysis)fixed-effect model matrix is rank deficient so dropping 8 columns / coefficientsdrop1(r8_step2, test = "Chisq")fixed-effect model matrix is rank deficient so dropping 6 columns / coefficients
fixed-effect model matrix is rank deficient so dropping 4 columns / coefficients
fixed-effect model matrix is rank deficient so dropping 8 columns / coefficientsSingle term deletions
Model:
score ~ character * factor * poly(age, 2) + poly(age, 3) + poly(age,
3):character + poly(age, 3):factor + (1 | subid)
Df AIC LRT Pr(Chi)
<none> 1450.8
character:poly(age, 3) 1 1448.9 0.04053 0.8405
factor:poly(age, 3) 2 1447.6 0.73750 0.6916
character:factor:poly(age, 2) 4 1445.0 2.20553 0.6980r8_step3 <- lmer(score ~ character * factor + poly(age, 3) + (1 | subid) + poly(age, 2) + character:poly(age, 2) + factor:poly(age, 2), scores_children_analysis)fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientsdrop1(r8_step3, test = "Chisq")fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientsSingle term deletions
Model:
score ~ character * factor + poly(age, 3) + (1 | subid) + poly(age,
2) + character:poly(age, 2) + factor:poly(age, 2)
Df AIC LRT Pr(Chi)
<none> 1439.9
poly(age, 3) 1 1439.2 1.334 0.24815
character:factor 2 1596.5 160.556 < 2.2e-16 ***
character:poly(age, 2) 2 1440.9 4.959 0.08379 .
factor:poly(age, 2) 4 1468.3 36.366 2.433e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1summary(r8_step3)Linear mixed model fit by REML ['lmerMod']
Formula: score ~ character * factor + poly(age, 3) + (1 | subid) + poly(age,
2) + character:poly(age, 2) + factor:poly(age, 2)
Data: scores_children_analysis
REML criterion at convergence: 1417
Scaled residuals:
Min 1Q Median 3Q Max
-3.7618 -0.5295 0.0828 0.6459 2.3109
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.07057 0.2656
Residual 0.63534 0.7971
Number of obs: 573, groups: subid, 191
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.005992 0.038533 -0.156
characterrobot -0.225922 0.038534 -5.863
factorF1 0.005961 0.047123 0.127
factorF3 0.028253 0.047123 0.600
poly(age, 3)1 -0.493244 0.924390 -0.534
poly(age, 3)2 1.519777 0.927227 1.639
poly(age, 3)3 -1.048164 0.923105 -1.135
characterrobot:factorF1 0.237441 0.047194 5.031
characterrobot:factorF3 0.411189 0.047194 8.713
characterrobot:poly(age, 2)1 -1.894450 0.927048 -2.044
characterrobot:poly(age, 2)2 0.740136 0.927297 0.798
factorF1:poly(age, 2)1 -5.988002 1.127742 -5.310
factorF3:poly(age, 2)1 5.879122 1.127742 5.213
factorF1:poly(age, 2)2 -0.220734 1.128439 -0.196
factorF3:poly(age, 2)2 0.731799 1.128439 0.649
Correlation matrix not shown by default, as p = 15 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itfit warnings:
fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients# # robot only
# robot_r4 <- lmer(score ~ factor + (1 | subid), scores_children_analysis %>% filter(character == "robot"))
# robot_r5 <- lmer(score ~ factor + scale(age) + (1 | subid), scores_children_analysis %>% filter(character == "robot"))
# robot_r6 <- lmer(score ~ factor * scale(age) + (1 | subid), scores_children_analysis %>% filter(character == "robot"))
# anova(robot_r4, robot_r5, robot_r6)
# # summary(robot_r4)
# # summary(robot_r5)
# summary(robot_r6)scores_children_plotting <- d %>%
select(subid, age, character) %>%
distinct() %>%
full_join(scores_children) %>%
mutate(character = factor(character)) %>%
filter(!is.na(score_F1), !is.na(score_F2), !is.na(score_F3), !is.na(age)) %>%
gather(factor, score, starts_with("score_")) %>%
mutate(factor = factor(factor)) %>%
multi_boot(column = "score",
summary_groups = c("age", "character", "factor"),
statistics_functions = c("mean", "ci_lower", "ci_upper"))Joining, by = "subid"
joining character vector and factor, coercing into character vector# plot
ggplot(scores_children_plotting %>%
ungroup() %>%
mutate(factor = factor(factor,
labels = c("Social-emotional",
"Physiological",
"Perceptual-cognitive"))),
aes(x = age, y = mean, color = character, fill = character, shape = character)) +
facet_wrap("factor", ncol = 3) +
theme_bw() +
theme(text = element_text(size = 28),
legend.position = "bottom") +
# geom_smooth(method = "loess", alpha = 0.4) +
geom_smooth(method = "lm", alpha = 0.4) +
geom_point(size = 2) +
scale_shape_manual(values = c(19, 15)) +
labs(title = "Factor scores by children's age",
# subtitle = "Children (Study 2)\n",
x = "Age (years)",
y = "Factor score") # 1000 by 500
Adults by age at test
scores_adults <- fa(d3_adult, nfactors = efa_comb_max_unrot_nfactors, rotate = rot_type,
cor = cor_type, scores = score_type)$scores %>%
data.frame() %>%
rownames_to_column(var = "subid") %>%
mutate(ageGroup = factor(ifelse(grepl("run", subid), "adult", "adult")))
colnames(scores_adults)[2:4] <- c("score_F1", "score_F2", "score_F3")# analyze
scores_adults_analysis <- d %>%
select(subid, age, character) %>%
distinct() %>%
left_join(scores_adults) %>%
mutate(character = factor(character)) %>%
filter(!is.na(score_F1) & !is.na(score_F2) & !is.na(score_F3), !is.na(age)) %>%
gather(factor, score, starts_with("score_")) %>%
mutate(factor = factor(factor))Joining, by = "subid"
joining character vector and factor, coercing into character vector# set contrasts
contrasts(scores_adults_analysis$factor) <- cbind(F1 = c(1, -1, 0), # MAKE SURE TO DOUBLE-CHECK!!
F3 = c(0, -1, 1))
contrasts(scores_adults_analysis$character) <- cbind(robot = c(-1, 1))
r4 <- lmer(score ~ character * factor + (1 | subid), scores_adults_analysis)
r5 <- lmer(score ~ character * factor + scale(age) + (1 | subid), scores_adults_analysis)
r6 <- lmer(score ~ character * factor * scale(age) + (1 | subid), scores_adults_analysis)
anova(r4, r5, r6)refitting model(s) with ML (instead of REML)Data: scores_adults_analysis
Models:
r4: score ~ character * factor + (1 | subid)
r5: score ~ character * factor + scale(age) + (1 | subid)
r6: score ~ character * factor * scale(age) + (1 | subid)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
r4 8 1310.7 1344.9 -647.33 1294.7
r5 9 1311.6 1350.2 -646.82 1293.6 1.0198 1 0.3126
r6 14 1317.7 1377.7 -644.83 1289.7 3.9724 5 0.5534# summary(r4)
# summary(r5)
summary(r6)Linear mixed model fit by REML ['lmerMod']
Formula: score ~ character * factor * scale(age) + (1 | subid)
Data: scores_adults_analysis
REML criterion at convergence: 1343.7
Scaled residuals:
Min 1Q Median 3Q Max
-4.1700 -0.4243 -0.0353 0.4173 5.0265
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.07636 0.2763
Residual 0.59300 0.7701
Number of obs: 537, groups: subid, 179
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.009176 0.039137 -0.234
characterrobot -0.244150 0.039137 -6.238
factorF1 -0.021288 0.047008 -0.453
factorF3 0.009262 0.047008 0.197
scale(age) -0.042074 0.039453 -1.066
characterrobot:factorF1 0.193142 0.047008 4.109
characterrobot:factorF3 0.473474 0.047008 10.072
characterrobot:scale(age) 0.023902 0.039453 0.606
factorF1:scale(age) -0.064585 0.047387 -1.363
factorF3:scale(age) 0.030715 0.047387 0.648
characterrobot:factorF1:scale(age) -0.022095 0.047387 -0.466
characterrobot:factorF3:scale(age) 0.057210 0.047387 1.207
Correlation of Fixed Effects:
(Intr) chrctr fctrF1 fctrF3 scl(g) chr:F1 chr:F3 chr:() fF1:() fF3:() c:F1:(
charactrrbt -0.017
factorF1 0.000 0.000
factorF3 0.000 0.000 -0.500
scale(age) 0.002 -0.016 0.000 0.000
chrctrrb:F1 0.000 0.000 -0.017 0.008 0.000
chrctrrb:F3 0.000 0.000 0.008 -0.017 0.000 -0.500
chrctrrb:() -0.016 0.002 0.000 0.000 -0.120 0.000 0.000
fctrF1:sc() 0.000 0.000 0.002 -0.001 0.000 -0.016 0.008 0.000
fctrF3:sc() 0.000 0.000 -0.001 0.002 0.000 0.008 -0.016 0.000 -0.500
chrct:F1:() 0.000 0.000 -0.016 0.008 0.000 0.002 -0.001 0.000 -0.120 0.060
chrct:F3:() 0.000 0.000 0.008 -0.016 0.000 -0.001 0.002 0.000 0.060 -0.120 -0.500# stepwise regression
drop1(r6, test = "Chisq")Single term deletions
Model:
score ~ character * factor * scale(age) + (1 | subid)
Df AIC LRT Pr(Chi)
<none> 1317.7
character:factor:scale(age) 2 1315.2 1.5134 0.4692r6_step2 <- lmer(score ~ character * factor + scale(age) + character:scale(age) + factor:scale(age) + (1 | subid), scores_adults_analysis)
drop1(r6_step2, test = "Chisq")Single term deletions
Model:
score ~ character * factor + scale(age) + character:scale(age) +
factor:scale(age) + (1 | subid)
Df AIC LRT Pr(Chi)
<none> 1315.2
character:factor 2 1482.0 170.875 <2e-16 ***
character:scale(age) 1 1313.5 0.375 0.5403
factor:scale(age) 2 1313.3 2.084 0.3528
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1summary(r6_step2)Linear mixed model fit by REML ['lmerMod']
Formula: score ~ character * factor + scale(age) + character:scale(age) +
factor:scale(age) + (1 | subid)
Data: scores_adults_analysis
REML criterion at convergence: 1336.4
Scaled residuals:
Min 1Q Median 3Q Max
-4.2024 -0.4299 -0.0289 0.4079 5.0794
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.07665 0.2769
Residual 0.59213 0.7695
Number of obs: 537, groups: subid, 179
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.009176 0.039137 -0.234
characterrobot -0.244150 0.039137 -6.238
factorF1 -0.021635 0.046967 -0.461
factorF3 0.010161 0.046967 0.216
scale(age) -0.042074 0.039453 -1.066
characterrobot:factorF1 0.193190 0.046973 4.113
characterrobot:factorF3 0.473351 0.046973 10.077
characterrobot:scale(age) 0.023902 0.039453 0.606
factorF1:scale(age) -0.067235 0.047010 -1.430
factorF3:scale(age) 0.037578 0.047010 0.799
Correlation of Fixed Effects:
(Intr) chrctr fctrF1 fctrF3 scl(g) chr:F1 chr:F3 chr:() fF1:()
charactrrbt -0.017
factorF1 0.000 0.000
factorF3 0.000 0.000 -0.500
scale(age) 0.002 -0.016 0.000 0.000
chrctrrb:F1 0.000 0.000 -0.017 0.008 0.000
chrctrrb:F3 0.000 0.000 0.008 -0.017 0.000 -0.500
chrctrrb:() -0.016 0.002 0.000 0.000 -0.120 0.000 0.000
fctrF1:sc() 0.000 0.000 0.000 0.000 0.000 -0.016 0.008 0.000
fctrF3:sc() 0.000 0.000 0.000 0.000 0.000 0.008 -0.016 0.000 -0.500# explore polynoial effects of age
r6b <- lmer(score ~ character * factor * poly(age, 1) + (1 | subid), scores_adults_analysis)
r7 <- lmer(score ~ character * factor * poly(age, 2) + (1 | subid), scores_adults_analysis)
r8 <- lmer(score ~ character * factor * poly(age, 3) + (1 | subid), scores_adults_analysis)
anova(r6b, r7, r8)refitting model(s) with ML (instead of REML)Data: scores_adults_analysis
Models:
r6b: score ~ character * factor * poly(age, 1) + (1 | subid)
r7: score ~ character * factor * poly(age, 2) + (1 | subid)
r8: score ~ character * factor * poly(age, 3) + (1 | subid)
Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq)
r6b 14 1317.7 1377.7 -644.83 1289.7
r7 20 1313.8 1399.5 -636.91 1273.8 15.8362 6 0.01466 *
r8 26 1325.2 1436.7 -636.62 1273.2 0.5744 6 0.99681
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1# summary(r6b)
summary(r7)Linear mixed model fit by REML ['lmerMod']
Formula: score ~ character * factor * poly(age, 2) + (1 | subid)
Data: scores_adults_analysis
REML criterion at convergence: 1279.6
Scaled residuals:
Min 1Q Median 3Q Max
-4.2507 -0.4023 -0.0506 0.3857 5.0127
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.08116 0.2849
Residual 0.57757 0.7600
Number of obs: 537, groups: subid, 179
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.011630 0.039235 -0.296
characterrobot -0.239682 0.039235 -6.109
factorF1 -0.014914 0.046538 -0.320
factorF3 0.006894 0.046538 0.148
poly(age, 2)1 -1.100333 0.923922 -1.191
poly(age, 2)2 -1.334111 0.938832 -1.421
characterrobot:factorF1 0.181652 0.046538 3.903
characterrobot:factorF3 0.484090 0.046538 10.402
characterrobot:poly(age, 2)1 0.762584 0.923922 0.825
characterrobot:poly(age, 2)2 0.687514 0.938832 0.732
factorF1:poly(age, 2)1 -1.167741 1.095896 -1.066
factorF3:poly(age, 2)1 0.566317 1.095896 0.517
factorF1:poly(age, 2)2 3.429681 1.113581 3.080
factorF3:poly(age, 2)2 -3.219533 1.113581 -2.891
characterrobot:factorF1:poly(age, 2)1 -1.049730 1.095896 -0.958
characterrobot:factorF3:poly(age, 2)1 1.808909 1.095896 1.651
characterrobot:factorF1:poly(age, 2)2 -1.787344 1.113581 -1.605
characterrobot:factorF3:poly(age, 2)2 0.572177 1.113581 0.514
Correlation matrix not shown by default, as p = 18 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itsummary(r8)Linear mixed model fit by REML ['lmerMod']
Formula: score ~ character * factor * poly(age, 3) + (1 | subid)
Data: scores_adults_analysis
REML criterion at convergence: 1267.5
Scaled residuals:
Min 1Q Median 3Q Max
-4.2629 -0.4037 -0.0591 0.3873 5.0404
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.08181 0.2860
Residual 0.58387 0.7641
Number of obs: 537, groups: subid, 179
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.011480 0.039628 -0.290
characterrobot -0.241353 0.039628 -6.090
factorF1 -0.014327 0.047025 -0.305
factorF3 0.006298 0.047025 0.134
poly(age, 3)1 -1.088609 0.964125 -1.129
poly(age, 3)2 -1.308965 0.964970 -1.356
poly(age, 3)3 0.480035 1.012121 0.474
characterrobot:factorF1 0.180395 0.047025 3.836
characterrobot:factorF3 0.483600 0.047025 10.284
characterrobot:poly(age, 3)1 0.653735 0.964125 0.678
characterrobot:poly(age, 3)2 0.613528 0.964970 0.636
characterrobot:poly(age, 3)3 0.058530 1.012121 0.058
factorF1:poly(age, 3)1 -1.128132 1.144074 -0.986
factorF3:poly(age, 3)1 0.528151 1.144074 0.462
factorF1:poly(age, 3)2 3.469080 1.145077 3.030
factorF3:poly(age, 3)2 -3.239854 1.145077 -2.829
factorF1:poly(age, 3)3 0.331936 1.201029 0.276
factorF3:poly(age, 3)3 0.179658 1.201029 0.150
characterrobot:factorF1:poly(age, 3)1 -1.132138 1.144074 -0.990
characterrobot:factorF3:poly(age, 3)1 1.777816 1.144074 1.554
characterrobot:factorF1:poly(age, 3)2 -1.848265 1.145077 -1.614
characterrobot:factorF3:poly(age, 3)2 0.557671 1.145077 0.487
characterrobot:factorF1:poly(age, 3)3 -0.094616 1.201029 -0.079
characterrobot:factorF3:poly(age, 3)3 0.204386 1.201029 0.170
Correlation matrix not shown by default, as p = 24 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itround(signif(summary(r6)$coefficients, 3), 2) %>% data.frame() # %>% View()# stepwise regression
drop1(r8, test = "Chisq")Single term deletions
Model:
score ~ character * factor * poly(age, 3) + (1 | subid)
Df AIC LRT Pr(Chi)
<none> 1325.2
character:factor:poly(age, 3) 6 1318.5 5.2973 0.5063r8_step2 <- lmer(score ~ character * factor * poly(age, 2) + poly(age, 3) + poly(age, 3):character + poly(age, 3):factor + (1 | subid), scores_adults_analysis)fixed-effect model matrix is rank deficient so dropping 8 columns / coefficientsdrop1(r8_step2, test = "Chisq")fixed-effect model matrix is rank deficient so dropping 6 columns / coefficients
fixed-effect model matrix is rank deficient so dropping 4 columns / coefficients
fixed-effect model matrix is rank deficient so dropping 8 columns / coefficientsSingle term deletions
Model:
score ~ character * factor * poly(age, 2) + poly(age, 3) + poly(age,
3):character + poly(age, 3):factor + (1 | subid)
Df AIC LRT Pr(Chi)
<none> 1321.3
character:poly(age, 3) 1 1319.3 0.0035 0.9528
factor:poly(age, 3) 2 1317.5 0.2499 0.8825
character:factor:poly(age, 2) 4 1318.5 5.2669 0.2610r8_step3 <- lmer(score ~ character * factor + poly(age, 3) + (1 | subid) + poly(age, 2) + character:poly(age, 2) + factor:poly(age, 2), scores_adults_analysis)fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientsdrop1(r8_step3, test = "Chisq")fixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
convergence code 3 from bobyqa: bobyqa -- a trust region step failed to reduce qfixed-effect model matrix is rank deficient so dropping 2 columns / coefficients
fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientsSingle term deletions
Model:
score ~ character * factor + poly(age, 3) + (1 | subid) + poly(age,
2) + character:poly(age, 2) + factor:poly(age, 2)
Df AIC LRT Pr(Chi)
<none> 1313.0
poly(age, 3) 1 1311.3 0.291 0.5898
character:factor 2 1483.7 174.660 <2e-16 ***
character:poly(age, 2) 2 1309.8 0.827 0.6613
factor:poly(age, 2) 4 1316.7 11.668 0.0200 *
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1summary(r8_step3)Linear mixed model fit by REML ['lmerMod']
Formula: score ~ character * factor + poly(age, 3) + (1 | subid) + poly(age,
2) + character:poly(age, 2) + factor:poly(age, 2)
Data: scores_adults_analysis
REML criterion at convergence: 1290.5
Scaled residuals:
Min 1Q Median 3Q Max
-4.3022 -0.4156 -0.0436 0.4020 5.1505
Random effects:
Groups Name Variance Std.Dev.
subid (Intercept) 0.08157 0.2856
Residual 0.57978 0.7614
Number of obs: 537, groups: subid, 179
Fixed effects:
Estimate Std. Error t value
(Intercept) -0.01126 0.03932 -0.286
characterrobot -0.24147 0.03946 -6.119
factorF1 -0.02148 0.04648 -0.462
factorF3 0.01001 0.04648 0.215
poly(age, 3)1 -1.07389 0.92720 -1.158
poly(age, 3)2 -1.29796 0.94327 -1.376
poly(age, 3)3 0.50036 0.94639 0.529
characterrobot:factorF1 0.18368 0.04661 3.940
characterrobot:factorF3 0.48260 0.04661 10.353
characterrobot:poly(age, 2)1 0.64590 0.95179 0.679
characterrobot:poly(age, 2)2 0.60599 0.95335 0.636
factorF1:poly(age, 2)1 -1.55316 1.07696 -1.442
factorF3:poly(age, 2)1 0.86662 1.07696 0.805
factorF1:poly(age, 2)2 2.92049 1.07991 2.704
factorF3:poly(age, 2)2 -2.84181 1.07991 -2.632
Correlation matrix not shown by default, as p = 15 > 12.
Use print(x, correlation=TRUE) or
vcov(x) if you need itfit warnings:
fixed-effect model matrix is rank deficient so dropping 2 columns / coefficientsscores_adults_plotting <- d %>%
filter(!is.na(age)) %>%
select(subid, age, character) %>%
distinct() %>%
full_join(scores_adults) %>%
mutate(character = factor(character)) %>%
filter(!is.na(score_F1), !is.na(score_F2), !is.na(score_F3), !is.na(age)) %>%
gather(factor, score, starts_with("score_")) %>%
mutate(factor = factor(factor)) %>%
multi_boot(column = "score",
summary_groups = c("age", "character", "factor"),
statistics_functions = c("mean", "ci_lower", "ci_upper"))Joining, by = "subid"
joining character vector and factor, coercing into character vector# plot
ggplot(scores_adults_plotting %>%
ungroup() %>%
mutate(factor = factor(factor,
labels = c("Social-emotional",
"Physiological",
"Perceptual-cognitive"))),
aes(x = age, y = mean, color = character, fill = character, shape = character)) +
facet_wrap("factor", ncol = 3) +
theme_bw() +
theme(text = element_text(size = 28),
legend.position = "bottom") +
# geom_smooth(method = "loess", alpha = 0.4) +
geom_smooth(method = "lm", alpha = 0.4) +
geom_point(size = 2) +
scale_shape_manual(values = c(19, 15)) +
labs(title = "Factor scores by adults's age",
# subtitle = "Adults (Study 2)\n",
x = "Age (years)",
y = "Factor score") # 1000 by 500
Big figure
# by condition
d1_bycond2 <- d2 %>%
left_join(d_child02 %>% select(subid, age)) %>%
select(character, capacity, capWording, responseNum, subid, ageGroup, age) %>%
filter(capacity != "na", is.na(responseNum) == F) %>%
mutate(capWording = gsub(" --.*", "", capWording),
ageGroup3 = ifelse(ageGroup == "adult", "adult",
ifelse(is.na(age), NA,
ifelse(age < 8, "7y",
ifelse(age < 9, "8y",
ifelse(age < 10, "9y",
NA)))))) %>%
distinct()Joining, by = c("subid", "age")
joining factors with different levels, coercing to character vectord1_bycond2 %>% select(ageGroup3, subid) %>% distinct() %>% count(ageGroup3)# make df for plotting
d1_bycond2_mb <- multi_boot(d1_bycond2,
column = "responseNum",
summary_groups = c("ageGroup3", "character", "capacity", "capWording"),
statistics_functions = c("mean", "ci_lower", "ci_upper"))
d1_bycond2_mb_factorsAll <- d1_bycond2_mb %>%
full_join(efa_comb_small_rot_loadings %>%
group_by(factor) %>%
mutate(order = rank(desc(loading_abs)))) %>%
arrange(character, factor, desc(loading_abs)) %>%
rownames_to_column(var = "full_order") %>%
mutate(full_order = as.numeric(full_order)) %>%
arrange(factorName, full_order) %>%
ungroup() %>%
filter(!is.na(ageGroup3)) %>%
mutate(factorName = factor(factorName,
levels = c("Factor 1", "Factor 2", "Factor 3"),
labels = c("Social-emotional",
"Physiological",
"Perceptual-cognitive")))Joining, by = c("capacity", "capWording")
joining character vector and factor, coercing into character vectordodge_width <- 2
ggplot(d1_bycond2_mb_factorsAll,
aes(x = desc(order*2), y = mean,
color = ageGroup3, shape = ageGroup3,
label = capWording)) +
facet_grid(factorName ~ character, scales = "free", space = "free") +
geom_hline(yintercept = 0, lty = 3) +
geom_hline(yintercept = 0.5, lty = 3) +
geom_hline(yintercept = 1, lty = 3) +
geom_point(stat = "identity", position = position_dodge(width = dodge_width), size = 8) +
scale_shape_manual(values = c(rep(18, 3), 17)) +
# scale_colour_brewer(type = "seq", palette = "PuRd") +
scale_colour_hue(h = c(0, 180)) +
geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper),
position = position_dodge(width = dodge_width), width = 0) +
geom_text(aes(y = -0.5, hjust = 0),
color = "black",
# color = d1_bycond2_mb_factorsAll$textColor,
size = 8) +
labs(title = "Responses by mental capacity item",
y = "Mean response (0 = NO, 0.5 = KINDA, 1 = YES)",
x = "Capacity",
color = "Age group: ", shape = "Age group: ") +
scale_y_continuous(breaks = c(0, 0.5, 1)) +
coord_flip() +
theme_bw() +
theme(text = element_text(size = 28),
# axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "top") # 1700 by 2000
# FILTERED BY ROBOT
# feel safe
with(d2 %>% filter(capWording == "feel_safe",
!is.na(responseCat),
character == "robot"),
table(ageGroup, responseCat)) #%>% responseCat
ageGroup no kinda yes
adult 82 15 3
child 33 22 40 #summary()
# feel tired
with(d2 %>% filter(capWording == "feel_tired",
!is.na(responseCat),
character == "robot"),
table(ageGroup, responseCat)) #%>% responseCat
ageGroup no kinda yes
adult 88 6 6
child 38 20 36 #summary()
# feel scared
with(d2 %>% filter(capWording == "feel_scared",
!is.na(responseCat),
character == "robot"),
table(ageGroup, responseCat)) #%>% responseCat
ageGroup no kinda yes
adult 93 4 3
child 50 18 28 #summary()
# get hungry
with(d2 %>% filter(capWording == "get_hungry",
!is.na(responseCat),
character == "robot"),
table(ageGroup, responseCat)) #%>% responseCat
ageGroup no kinda yes
adult 96 1 2
child 74 5 15 #summary()
# feel pain
with(d2 %>% filter(capWording == "feel_pain",
!is.na(responseCat),
character == "robot"),
table(ageGroup, responseCat)) #%>% responseCat
ageGroup no kinda yes
adult 94 4 2
child 65 12 17 #summary()
# feel proud
proud_table <- with(d2 %>% filter(capWording == "feel_proud",
!is.na(responseCat),
character == "robot") %>%
mutate(ageGroup3 = ifelse(ageGroup == "adult", "adult",
ifelse(is.na(age), NA,
ifelse(age < 8, "7",
ifelse(age < 9, "8",
"9"))))),
table(ageGroup3, responseCat)) %>%
prop.table(1)
proud_table responseCat
ageGroup3 no kinda yes
7 0.16666667 0.13888889 0.69444444
8 0.33333333 0.21212121 0.45454545
9 0.45833333 0.20833333 0.33333333
adult 0.90099010 0.08910891 0.00990099proud_table[3,2]+proud_table[3,3][1] 0.5416667# feel happy
happy_table <- with(d2 %>% filter(capWording == "feel_happy",
!is.na(responseCat),
character == "robot") %>%
mutate(ageGroup3 = ifelse(ageGroup == "adult", "adult",
ifelse(is.na(age), NA,
ifelse(age < 8, "7",
ifelse(age < 9, "8",
"9"))))),
table(ageGroup3, responseCat)) %>%
prop.table(1)
happy_table responseCat
ageGroup3 no kinda yes
7 0.16666667 0.11111111 0.72222222
8 0.28571429 0.17142857 0.54285714
9 0.41666667 0.16666667 0.41666667
adult 0.89108911 0.03960396 0.06930693happy_table[3,2]+happy_table[3,3][1] 0.5833333# feel guilty
guilty_table <- with(d2 %>% filter(capWording == "feel_guilty",
!is.na(responseCat),
character == "robot") %>%
mutate(ageGroup3 = ifelse(ageGroup == "adult", "adult",
ifelse(is.na(age), NA,
ifelse(age < 8, "7",
ifelse(age < 9, "8",
"9"))))),
table(ageGroup3, responseCat)) %>%
prop.table(1)
guilty_table responseCat
ageGroup3 no kinda yes
7 0.4594595 0.2162162 0.3243243
8 0.4705882 0.2058824 0.3235294
9 0.5000000 0.2500000 0.2500000
adult 0.9700000 0.0300000 0.0000000guilty_table[1,2]+guilty_table[1,3][1] 0.5405405guilty_table[2,2]+guilty_table[2,3][1] 0.5294118guilty_table[3,2]+guilty_table[3,3][1] 0.5clm_guilty <- ordinal::clm(responseCat ~ age,
data = d2 %>%
filter(capWording == "feel_guilty",
!is.na(responseCat),
character == "robot"))
summary(clm_guilty)formula: responseCat ~ age
data: d2 %>% filter(capWording == "feel_guilty", !is.na(responseCat), character == "robot")
link threshold nobs logLik AIC niter max.grad cond.H
logit flexible 191 -113.71 233.42 7(0) 1.42e-10 1.2e+03
Coefficients:
Estimate Std. Error z value Pr(>|z|)
age -0.1834 0.0362 -5.065 4.09e-07 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Threshold coefficients:
Estimate Std. Error z value
no|kinda -1.6428 0.4161 -3.948
kinda|yes -0.6527 0.4109 -1.588
(4 observations deleted due to missingness)# personality
personality_table <- with(d2 %>% filter(capWording == "have_a_personality",
!is.na(responseCat),
character == "robot") %>%
mutate(ageGroup3 = ifelse(ageGroup == "adult", "adult",
ifelse(is.na(age), NA,
ifelse(age < 8, "7",
ifelse(age < 9, "8",
"9"))))),
table(ageGroup3, responseCat)) %>%
prop.table(1)
personality_table responseCat
ageGroup3 no kinda yes
7 0.25000000 0.22222222 0.52777778
8 0.23529412 0.32352941 0.44117647
9 0.29166667 0.33333333 0.37500000
adult 0.62745098 0.30392157 0.06862745personality_table[3,2]+personality_table[3,3][1] 0.7083333# self-control
self_control_table <- with(d2 %>% filter(capWording == "have_self-control",
!is.na(responseCat),
character == "robot") %>%
mutate(ageGroup3 = ifelse(ageGroup == "adult", "adult",
ifelse(is.na(age), NA,
ifelse(age < 8, "7",
ifelse(age < 9, "8",
"9"))))),
table(ageGroup3, responseCat)) %>%
prop.table(1)
self_control_table responseCat
ageGroup3 no kinda yes
7 0.2777778 0.2222222 0.5000000
8 0.2857143 0.2857143 0.4285714
9 0.3333333 0.1666667 0.5000000
adult 0.5643564 0.2673267 0.1683168self_control_table[1,2]+self_control_table[1,3][1] 0.7222222self_control_table[2,2]+self_control_table[2,3][1] 0.7142857self_control_table[3,2]+self_control_table[3,3][1] 0.6666667clm_self_control <- ordinal::clm(responseCat ~ age,
data = d2 %>%
filter(capWording == "have_self-control",
!is.na(responseCat),
character == "robot"))
summary(clm_self_control)formula: responseCat ~ age
data:
d2 %>% filter(capWording == "have_self-control", !is.na(responseCat), character == "robot")
link threshold nobs logLik AIC niter max.grad cond.H
logit flexible 192 -198.17 402.34 4(0) 2.02e-08 3.6e+03
Coefficients:
Estimate Std. Error z value Pr(>|z|)
age -0.03914 0.01013 -3.863 0.000112 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Threshold coefficients:
Estimate Std. Error z value
no|kinda -1.12987 0.25948 -4.354
kinda|yes -0.02018 0.24554 -0.082
(4 observations deleted due to missingness)# MORE GENERAL
at_least_kinda_9y <- d2 %>%
filter(!is.na(responseCat),
ageGroup == "child",
!is.na(age),
age > 9) %>%
distinct() %>%
count(character, capacity, responseCat) %>%
group_by(character, capacity) %>%
mutate(prop_n = n/sum(n, na.rm = T)) %>%
select(-n) %>%
spread(responseCat, prop_n) %>% # n) %>%
mutate(at_least_kinda = sum(kinda, yes)) %>%
left_join(efa_comb_small_rot_loadings) %>%
arrange(factor, desc(at_least_kinda))Joining, by = "capacity"
joining character vector and factor, coercing into character vectorat_least_kinda_9y %>%
select(character:at_least_kinda, factor, loading)at_least_kinda_adult <- d2 %>%
filter(!is.na(responseCat),
character == "robot",
ageGroup == "adult") %>%
distinct() %>%
count(capacity, responseCat) %>%
group_by(capacity) %>%
mutate(prop_n = n/sum(n, na.rm = T)) %>%
select(-n) %>%
spread(responseCat, prop_n) %>% # n) %>%
mutate(at_least_kinda = sum(kinda, yes)) %>%
left_join(efa_comb_small_rot_loadings) %>%
arrange(factor, desc(at_least_kinda))Joining, by = "capacity"
joining character vector and factor, coercing into character vectorat_least_kinda_adult %>%
select(capacity:at_least_kinda, factor, loading)BIG TABLE
# make dataframe
big_table <- efa_adult_small_rot_loadings %>%
setNames(paste0('adult_', names(.))) %>%
rename(capacity = adult_capacity,
capWording = adult_capWording) %>%
full_join(efa_child_small_rot_loadings %>%
setNames(paste0('child_', names(.))) %>%
rename(capacity = child_capacity,
capWording = child_capWording)) %>%
full_join(efa_comb_small_rot_loadings %>%
setNames(paste0('comb_', names(.))) %>%
rename(capacity = comb_capacity,
capWording = comb_capWording)) %>%
arrange(comb_factor, desc(comb_loading_abs)) %>%
mutate(capWording = gsub("_", " ", capWording)) %>%
select(capWording,
adult_F2, child_F1, comb_F1, # double-check adults!
adult_F1, child_F2, comb_F2, # double-check adults!
adult_F3, child_F3, comb_F3) %>%
column_to_rownames(var = "capWording") %>%
signif(3) %>%
round(2)Joining, by = c("capacity", "capWording")
Joining, by = c("capacity", "capWording")big_tablecor.plot(scores_all %>% column_to_rownames(var = "subid") %>% select(-ageGroup))
cor.test(scores_all$score_F1, scores_all$score_F2, use = "complete.obs")
Pearson's product-moment correlation
data: scores_all$score_F1 and scores_all$score_F2
t = 0.37343, df = 373, p-value = 0.709
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.08209982 0.12036735
sample estimates:
cor
0.01933196 cor.test(scores_all$score_F1, scores_all$score_F3, use = "complete.obs")
Pearson's product-moment correlation
data: scores_all$score_F1 and scores_all$score_F3
t = 0.2175, df = 373, p-value = 0.8279
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.09011296 0.11240371
sample estimates:
cor
0.01126085 cor.test(scores_all$score_F2, scores_all$score_F3, use = "complete.obs")
Pearson's product-moment correlation
data: scores_all$score_F2 and scores_all$score_F3
t = -0.17088, df = 373, p-value = 0.8644
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
-0.11001990 0.09250651
sample estimates:
cor
-0.008847426 cor.ci(scores_adults %>% column_to_rownames(var = "subid") %>% select(-ageGroup))Call:corCi(x = x, keys = keys, n.iter = n.iter, p = p, overlap = overlap,
poly = poly, method = method, plot = plot)
Coefficients and bootstrapped confidence intervals
sc_F1 sc_F2 sc_F3
score_F1 1.00
score_F2 0.06 1.00
score_F3 0.03 -0.03 1.00
scale correlations and bootstrapped confidence intervals
lower.emp lower.norm estimate upper.norm upper.emp p
sc_F1-sc_F2 -0.08 -0.09 0.06 0.20 0.19 0.47
sc_F1-sc_F3 -0.06 -0.06 0.03 0.13 0.13 0.50
sc_F2-sc_F3 -0.13 -0.14 -0.03 0.11 0.09 0.80
cor.ci(scores_children %>% column_to_rownames(var = "subid") %>% select(-ageGroup))Call:corCi(x = x, keys = keys, n.iter = n.iter, p = p, overlap = overlap,
poly = poly, method = method, plot = plot)
Coefficients and bootstrapped confidence intervals
sc_F1 sc_F2 sc_F3
score_F1 1.00
score_F2 0.00 1.00
score_F3 0.01 0.00 1.00
scale correlations and bootstrapped confidence intervals
lower.emp lower.norm estimate upper.norm upper.emp p
sc_F1-sc_F2 -0.15 -0.16 0.00 0.15 0.14 0.92
sc_F1-sc_F3 -0.14 -0.12 0.01 0.14 0.14 0.88
sc_F2-sc_F3 -0.11 -0.11 0.00 0.15 0.13 0.80
cor.ci(scores_all %>% column_to_rownames(var = "subid") %>% select(-ageGroup))Call:corCi(x = x, keys = keys, n.iter = n.iter, p = p, overlap = overlap,
poly = poly, method = method, plot = plot)
Coefficients and bootstrapped confidence intervals
sc_F1 sc_F2 sc_F3
score_F1 1.00
score_F2 0.02 1.00
score_F3 0.01 -0.01 1.00
scale correlations and bootstrapped confidence intervals
lower.emp lower.norm estimate upper.norm upper.emp p
sc_F1-sc_F2 -0.07 -0.09 0.02 0.13 0.12 0.69
sc_F1-sc_F3 -0.09 -0.09 0.01 0.10 0.07 0.86
sc_F2-sc_F3 -0.09 -0.11 -0.01 0.08 0.08 0.79
# by condition
d1_bycond3 <- d2 %>%
left_join(d_child02 %>% select(subid, age)) %>%
select(character, capacity, capWording, responseCat, subid, ageGroup, age) %>%
filter(capacity != "na", !is.na(responseCat)) %>%
mutate(capWording = gsub(" --.*", "", capWording),
ageGroup3 = ifelse(ageGroup == "adult", "adult",
ifelse(is.na(age), NA,
ifelse(age < 8, "7y",
ifelse(age < 9, "8y",
ifelse(age < 10, "9y",
NA)))))) %>%
distinct()Joining, by = c("subid", "age")
joining factors with different levels, coercing to character vectord1_bycond3 %>% select(ageGroup3, subid) %>% distinct() %>% count(ageGroup3)# make df for plotting
d1_bycond3_mb_factorsAll <- d1_bycond3 %>%
full_join(efa_comb_small_rot_loadings %>%
group_by(factor) %>%
mutate(order = rank(desc(loading_abs)))) %>%
arrange(character, factor, desc(loading_abs)) %>%
rownames_to_column(var = "full_order") %>%
mutate(full_order = as.numeric(full_order)) %>%
arrange(factorName, full_order) %>%
ungroup() %>%
filter(!is.na(ageGroup3)) %>%
mutate(factorName = factor(factorName,
levels = c("Factor 1", "Factor 2", "Factor 3"),
labels = c("Social-emotional",
"Physiological",
"Perceptual-cognitive")),
agentic = ifelse(capWording %in% c("decide_what_to_do", "make_choices", "have_self_control",
"make_plans", "have_goals",
"understand_how_somebody_else_is_feeling",
"know_what's_nice_and_what's_mean", "have_thoughts",
"remember_things", "communicate_with_somebody_else"),
TRUE, FALSE))Joining, by = c("capacity", "capWording")
joining character vector and factor, coercing into character vectord1_bycond3_annotate <- d1_bycond3_mb_factorsAll %>%
filter(order <= 4) %>%
select(character, order, capWording) %>%
distinct()
# ggplot(d1_bycond3_mb_factorsAll %>% filter(order <= 4),
# aes(x = interaction(ageGroup3, desc(order*2)),
# # y = responseCat,
# # color = ageGroup3, shape = ageGroup3,
# fill = responseCat,
# label = ageGroup3)) +
# # label = gsub("_", " ", paste(capWording, ageGroup3, sep = ": ")))) +
# # facet_grid(factorName ~ character * order, scales = "free", space = "free") +
# facet_grid(factorName ~ character, scales = "free", space = "free") +
# geom_bar(position = "fill") +
# geom_text(aes(y = -0.5, hjust = 0),
# color = "black",
# # color = d1_bycond2_mb_factorsAll$textColor,
# size = 8) +
# geom_text(aes(x = order, y = 1.5, label = capWording), hjust = -1) +
# # annotate("text", aes(facet = order, label = d1_bycond3_annotate$capWording)) +
# labs(title = "Responses by mental capacity item",
# y = "Relative frequency",
# x = "Capacity * Age group",
# fill = "Response: ") +
# scale_y_continuous(breaks = c(0, 0.5, 1)) +
# # coord_flip() +
# theme_bw() +
# theme(text = element_text(size = 28),
# # axis.title.y = element_blank(),
# axis.text.y = element_blank(),
# axis.ticks.y = element_blank(),
# legend.position = "top") # 1700 by 2000
ggplot(d1_bycond3_mb_factorsAll %>%
filter(order <= 5) %>%
mutate(facet = tools::toTitleCase(paste0(character, " (", ageGroup, ")"))),
# mutate(facet = tools::toTitleCase(paste(ageGroup, character, sep = ": "))),
aes(x = desc(order*2),
fill = character,
alpha = responseCat,
# fill = responseCat,
label = gsub("_", " ", capWording))) +
facet_grid(factorName ~ facet) + #, scales = "free", space = "free") +
geom_bar(position = "stack", color = "black") +
scale_alpha_manual(values=c(0.4, 0.7, 1)) +
geom_text(aes(y = 5, hjust = 0),
color = "black",
# color = d1_bycond2_mb_factorsAll$textColor,
size = 8) +
labs(title = "Responses by mental capacity item",
y = "Count",
x = "Capacity",
alpha = "Response: ",
fill = "Character: ") +
coord_flip() +
theme_bw() +
theme(text = element_text(size = 28),
# axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "top") # 2000 by 1200
ggplot(d1_bycond3_mb_factorsAll %>%
# filter(order <= 10) %>%
# filter(order <= 5 | capWording %in% c("know_what's_nice_and_what's_mean",
# "make_choices")) %>% # add selected agency by factor
mutate(facet = tools::toTitleCase(paste0(character, " (", ageGroup, ")"))),
# mutate(facet = tools::toTitleCase(paste(ageGroup, character, sep = ": "))),
aes(x = desc(order*2),
fill = character,
alpha = responseCat,
# fill = responseCat,
label = gsub("_", " ", capWording))) +
facet_grid(factorName ~ facet, scales = "free", space = "free") +
geom_bar(position = "stack", color = "black") +
scale_alpha_manual(values=c(0.4, 0.7, 1)) +
geom_text(aes(y = 5, hjust = 0),
color = "black",
# color = d1_bycond2_mb_factorsAll$textColor,
size = 8) +
labs(title = "Responses by mental capacity item",
y = "Count",
x = "Capacity",
alpha = "Response: ",
fill = "Character: ") +
coord_flip() +
theme_bw() +
theme(text = element_text(size = 32),
# axis.title.y = element_blank(),
axis.text.y = element_blank(),
axis.ticks.y = element_blank(),
legend.position = "top") # 2000 by 1200
d_srcd <- d1_bycond3_mb_factorsAll %>%
filter(factor == "F1", order <= 5) %>%
select(ageGroup, character, responseCat, order, capWording) %>%
mutate(labeller = ifelse(responseCat == "no", gsub("_", " ", capWording), NA)) %>%
distinct()
ggplot(d1_bycond3_mb_factorsAll %>%
filter(factor == "F1", order <= 5) %>%
mutate(facet = tools::toTitleCase(paste(ageGroup, character, sep = ": "))),
aes(x = desc(order*2),
fill = character,
alpha = responseCat,
label = gsub("_", " ", capWording))) +
facet_grid(factorName ~ facet) + #, scales = "free", space = "free") +
geom_bar(position = "stack", color = "black") +
scale_alpha_manual(values=c(0.2, 0.5, 1)) +
# scale_alpha_manual(values=c(0.4, 0.7, 1)) +
geom_text(data = d_srcd,
aes(x = desc(order*2), label = labeller, y = 5),
hjust = 0, angle = 90, color = "black", size = 8, alpha = 1) +
labs(title = "Responses by mental capacity item",
y = "Count",
x = "Capacity",
alpha = "Response: ",
fill = "Character: ") +
theme_bw() +
theme(text = element_text(size = 28),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "bottom") # 1000 by 500
d_srcd2 <- d1_bycond3_mb_factorsAll %>%
filter(factor == "F1", order <= 5, character == "robot") %>%
select(ageGroup, character, responseCat, order, capWording) %>%
mutate(labeller = ifelse(responseCat == "no", gsub("_", " ", capWording), NA)) %>%
distinct()
ggplot(d1_bycond3_mb_factorsAll %>%
filter(factor == "F1", order <= 5) %>%
mutate(facet = tolower(paste(ageGroup, character, sep = ": "))) %>%
mutate(facet = factor(facet,
levels = c("child: beetle", "child: robot",
"adult: beetle", "adult: robot"),
labels = c("children: beetle", "children: robot",
"adults: beetle", "adults: robot"))) %>%
mutate(ageGroup2 = factor(ageGroup, levels = c("child", "adult"),
labels = c("children", "adults"))),
aes(x = desc(order*2),
fill = character,
alpha = responseCat,
label = gsub("_", " ", capWording))) +
facet_grid(~ facet) + #, scales = "free", space = "free") +
geom_bar(position = "stack", color = "black") +
scale_alpha_manual(values=c(0.2, 0.5, 1)) +
# scale_alpha_manual(values=c(0.4, 0.7, 1)) +
geom_text(data = d_srcd2,
aes(x = desc(order*2), label = labeller, y = 5),
hjust = 0, angle = 90, color = "black", size = 8, alpha = 1) +
labs(y = "Count",
x = "Capacity",
alpha = "Response: ",
fill = "Character: ") +
labs(title = "Responses (raw counts)",
y = "Count",
x = "Capacity",
alpha = "response: ",
fill = "character: ") +
theme_bw() +
theme(text = element_text(size = 28),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "bottom") # 1000 by 500
ggplot(d1_bycond3_mb_factorsAll %>%
filter(factor == "F1", order <= 5, character == "robot") %>%
mutate(facet = tools::toTitleCase(paste(ageGroup, character, sep = ": "))) %>%
mutate(facet = factor(facet,
levels = c("Child: Robot", "Adult: Robot"),
labels = c("Children: robot", "Adults: robot"))) %>%
mutate(ageGroup2 = factor(ageGroup, levels = c("child", "adult"),
labels = c("children", "adults"))),
aes(x = desc(order*2),
alpha = responseCat,
label = gsub("_", " ", capWording))) +
facet_grid(~ ageGroup2) + #, scales = "free", space = "free") +
geom_bar(position = "stack", color = "black", fill = "#00BFC4") +
scale_alpha_manual(values=c(0.2, 0.5, 1)) +
# scale_alpha_manual(values=c(0.4, 0.7, 1)) +
geom_text(data = d_srcd2,
aes(x = desc(order*2), label = labeller, y = 5),
hjust = 0, angle = 90, color = "black", size = 8, alpha = 1) +
labs(y = "Count",
x = "Capacity",
alpha = "Response: ",
fill = "Character: ") +
theme_bw() +
theme(text = element_text(size = 28),
axis.text.x = element_blank(),
axis.ticks.x = element_blank(),
legend.position = "bottom") # 600 by 500
---
title: "Dimkid analysis"
author: "Kara Weisman"
date: "1/22/2017"
output:
  html_notebook: default
  html_document: default
---

# Preliminaries

## Set up environment

```{r prelim}
library(tidyverse)
library(langcog)
library(stats)
library(psych)
library(GPArotation)
library(lme4)
library(RColorBrewer)

# clear environment
rm(list=ls())
graphics.off()
```

## Read in data

```{r reading}
# ADULT run 01 (3-point scale)
d_adult01 <- read.csv("/Users/kweisman/Documents/Research (Stanford)/Projects/Dimkid/dimkid/data/adults/us_run-01_2016-06-05_anonymized.csv") %>% select(-X)

# # ADULT run 02 (7-point scale)
# d_adult02 <- read.csv("/Users/kweisman/Documents/Research (Stanford)/Projects/Dimkid/dimkid/data/adults/us_run-02_2016-07-19_anonymized.csv") %>% select(-X)
# 
# # ADULT run 03 (3-point scale, original wording for 'free will' and 'intentions')
# d_adult03 <- read.csv("/Users/kweisman/Documents/Research (Stanford)/Projects/Dimkid/dimkid/data/adults/us_run-03_2016-12-08_anonymized.csv") %>% select(-X)

# CHILD run 01 [lydia, olivia, allie (summer 2016) + nicky, dru, ariel, olivia (fall 2016) + campbell (winter 2017)]
d_child01 <- read.csv("/Users/kweisman/Documents/Research (Stanford)/Projects/Dimkid/dimkid/data/children/run-01_2017-01-19_anonymized.csv") %>% select(-X)

# demographic info (for children)
child_demo <- read.csv("/Users/kweisman/Documents/Research (Stanford)/Projects/Dimkid/dimkid/data/children/dimkid_participant_ages_2017-01-19.csv")
```

## Tidy data

```{r tidying}
# tidy adult run 01
d_adult02 <- d_adult01 %>%
  select(subid, age, gender, ethnicity, duration,
         charName, trialNum, capacity, capWording, 
         hoverTime, rt, response, responseNum) %>%
  rename(character = charName,
         sessionDuration = duration) %>%
  mutate(ageGroup = "adult")

# tidy demographics
child_demo_tidy <- child_demo %>%
  mutate(subid = gsub(" ", "", subid)) %>%
  mutate(subid = gsub("-", "", subid)) %>%
  mutate(subid = toupper(subid)) %>%
  select(subid, age, gender, ethnicity)

# tidy child run 01
d_child02 <- d_child01 %>%
  filter(character %in% c("beetle", "robot"))  %>% # no elephant condition! (n = 1 child)
  select(subid, sessionDuration,
         character, trialNum, capacity, capWording, 
         hoverTime, rt, response, responseNum) %>%
  mutate(ageGroup = "child") %>%
  mutate(subid = gsub(" ", "", subid)) %>%
  mutate(subid = gsub("-", "", subid)) %>%
  mutate(subid = toupper(subid)) %>%
  left_join(child_demo_tidy) %>%
  mutate(subid = factor(as.character(subid)))

# combine datasets
d <- d_adult02 %>%
  full_join(d_child02) %>%
  mutate(subid = factor(subid),
         character = factor(character),
         capWording = factor(gsub("\\ ", "_", gsub(" --.*", "", capWording))),
         responseCat = factor(response, levels = c("no", "kinda", "yes")),
         responseNum = ifelse(response == "no", 0,
                              ifelse(response == "kinda", 0.5,
                                     ifelse(response == "yes", 1,
                                            NA))),
         ageGroup = factor(ageGroup, levels = c("adult", "child"))) %>%
  distinct()
```
# Demographics

```{r n participants}
# total n
d %>% 
  select(ageGroup, subid, age) %>%
  distinct(.keep_all = T) %>%
  mutate(ageGroup2 = ifelse(ageGroup == "adult",
                            "adult",
                            ifelse(age < 7 | age > 10, 
                                   "out of age range",
                                   ifelse(is.na(age), 
                                          "missing age",
                                          "child")))) %>%
  count(ageGroup2)

# drop children outside of target age range (7-9y)
d1 <- d %>%
  filter((age >= 7 & age <= 10) | ageGroup == "adult")

d1 %>% 
  select(ageGroup, subid, age) %>%
  distinct(.keep_all = T) %>%
    mutate(ageGroup2 = ifelse(ageGroup == "adult",
                            "adult",
                            ifelse(age < 7 | age > 10, 
                                   "out of age range",
                                   ifelse(is.na(age), 
                                          "missing age",
                                          "child")))) %>%
  count(ageGroup2)
```

```{r condition}
# condition
d1 %>% 
  select(ageGroup, subid, character) %>%
  distinct(.keep_all = T) %>%
  count(ageGroup, character)
```

```{r age}
# age 
d1 %>% 
  select(ageGroup, subid, age) %>%
  distinct(.keep_all = T) %>%
  group_by(ageGroup) %>%
  summarise(mean_age = mean(age, na.rm = T),
            sd_age = sd(age, na.rm = T),
            median_age = median(age, na.rm = T),
            min_age = min(age, na.rm = T),
            max_age = max(age, na.rm = T))

# plot children's age
qplot(d1 %>% 
        filter(ageGroup == "child") %>%
        distinct(subid, .keep_all = T) %>% 
        select(age), bins = 18) +
  geom_vline(xintercept = median(d1$age[d1$ageGroup == "child"], na.rm = T), color = "red")

# check children's age by condition
d1 %>% 
  filter(ageGroup == "child") %>%
  distinct(subid, .keep_all = T) %>% 
  select(age, character) %>%
  group_by(character) %>% 
  summarise(median = median(age, na.rm = T))

t.test(age ~ character, 
       d1 %>% 
         filter(ageGroup == "child") %>%
         select(subid, age, character) %>% 
         distinct)

ggplot(d1 %>%
         filter(ageGroup == "child") %>%
         distinct(subid, .keep_all = T) %>% 
         select(age, character) %>%
         group_by(character) %>%
         mutate(median_age = median(age, na.rm = T)),
       aes(x = age)) +
  geom_histogram(bins = 9) +
  facet_wrap(~ character) +
  geom_vline(xintercept = median(d$age[d$ageGroup == "child"], na.rm = T), color = "black") +
  geom_vline(aes(xintercept = median_age, color = character), lty = 2)
```

```{r duration}
# duration
d1 %>%
  group_by(ageGroup) %>%
  summarise(mean = mean(sessionDuration, na.rm = T),
            median = median(sessionDuration, na.rm = T),
            min = min(sessionDuration, na.rm = T),
            max = max(sessionDuration, na.rm = T))

# plot duration by age group and character
ggplot(d1, aes(sessionDuration)) +
  facet_grid(character ~ ageGroup) +
  geom_histogram(breaks = 0:15)

t.test(sessionDuration ~ character,
       d1 %>%
         filter(ageGroup == "child") %>%
         select(subid, character, sessionDuration) %>%
         left_join(d %>% select(subid, sessionDuration)) %>%
         mutate(subid = as.character(subid),
                character = factor(character),
                sessionDuration = round(as.numeric(as.character(sessionDuration)), 3)) %>%
         distinct())

t.test(sessionDuration ~ character,
       d1 %>%
         filter(ageGroup == "adult") %>%
         select(subid, character, sessionDuration) %>%
         left_join(d %>% select(subid, sessionDuration)) %>%
         mutate(subid = as.character(subid),
                character = factor(character),
                sessionDuration = round(as.numeric(as.character(sessionDuration)), 3)) %>%
         distinct())
```

```{r gender}
# gender
d1 %>%
  select(ageGroup, subid, gender) %>%
  distinct(.keep_all = T) %>%
  count(ageGroup, gender)
```

```{r ethnicity}
# ethnicity
d1 %>% 
  select(ageGroup, subid, ethnicity) %>%
  mutate(ethnicity = tolower(ethnicity)) %>%
  mutate(black = grepl("black", ethnicity) |
           grepl("african american", ethnicity),
         east_asian = grepl("east asian", ethnicity) |
           grepl("eastasian", ethnicity) |
           grepl("chinese", ethnicity) |
           grepl("china", ethnicity) |
           grepl("korea", ethnicity) |
           grepl("japan", ethnicity) |
           grepl("taiwan", ethnicity),
         south_asian = grepl("south asian", ethnicity) |
           grepl("southasian", ethnicity) |
           grepl("india", ethnicity) |
           grepl("pakistan", ethnicity) |
           grepl("bangla", ethnicity) |
           grepl("sri lanka", ethnicity),
         latino = grepl("latin", ethnicity) |
           grepl("hispanic", ethnicity) |
           grepl("mexic", ethnicity),
         middle_eastern = grepl("middle", ethnicity),
         pac_island = grepl("pacific", ethnicity) |
           grepl("hawaii", ethnicity),
         native_am = grepl("nativeamerican", ethnicity) |
           grepl("native american", ethnicity) |
           grepl("american indian", ethnicity) |
           grepl("alaska native", ethnicity),
         white = grepl("white", ethnicity),
         other = grepl("other", ethnicity)) %>%
  distinct(.keep_all = T) %>%
  gather(ethnicityTF, TF, -subid, -ethnicity, -ageGroup) %>%
  filter(TF) %>%
  count(ageGroup, ethnicityTF)

# NOTE: not mutually exclusive!!

# mutually exclusive version
d1 %>% 
  select(ageGroup, subid, ethnicity) %>%
  mutate(ethnicity = tolower(ethnicity)) %>%
  distinct(.keep_all = T) %>%
  mutate(ethnicity2 = ifelse(ageGroup == "adult",
                             ifelse(grepl(",", ethnicity), "multiple", ethnicity),
                             ifelse(grepl(";", ethnicity), "multiple", ethnicity))) %>% 
  mutate(ethnicity3 = ifelse(grepl("other", ethnicity2),
                             ifelse(grepl("/", ethnicity2) | grepl("mix", ethnicity2),
                                    "multiple",
                                    ifelse(grepl("asian", ethnicity2) | grepl("korean", ethnicity2),
                                           "east asian",
                                           ethnicity2)),
                             ethnicity2)) %>%
  count(ageGroup, ethnicity3) %>%
  ungroup() %>%
  group_by(ageGroup) %>%
  mutate(prop = n/sum(n)) %>%
  arrange(ageGroup, desc(n))
```

```{r missing data}
# experimineter error
d_child01 %>% # use d_child01 (before dropping n = 1 child in "elephant" condition)
  filter(!(character %in% c("beetle", "robot"))) %>%
  select(subid) %>%
  distinct() %>%
  count() %>%
  mutate(percent = n/200)

# incomplete trials ("skip", "bail")
d1 %>% # use d1 (before dropping trials with rt > 250ms) 
  count(ageGroup, subid) %>%
  filter(n != 40) %>%
  mutate(n_missing = 40-n) %>%
  group_by(ageGroup) %>%
  summarise(sum_missing = sum(n_missing)) %>%
  mutate(percent = sum_missing/8000)

# trials with rt > 250ms
d1 %>%
  filter(rt < 250) %>%
  count(ageGroup) %>%
  mutate(percent = n/8000)

# all missing trials
d1 %>% # use d1 (before dropping trials) 
  count(ageGroup, subid) %>%
  filter(n != 40) %>%
  mutate(n_NA = 40-n) %>%
  group_by(ageGroup) %>%
  summarise(n_NA = sum(n_NA)) %>%
  mutate(percent_NA = n_NA/8000) %>%
  full_join(d1 %>%
              filter(rt < 250) %>%
              count(ageGroup) %>%
              rename(n_fast = n) %>%
              mutate(percent_fast = n_fast/8000)) %>%
  mutate(n_missing_TOTAL = n_NA + n_fast,
         percent_missing_TOTAL = percent_NA + percent_fast)
  
```

# Data preparation

## Filter RTs

```{r filter RTs, prep dataframe}
# examine and filter by RTs
ggplot(d1) +
  geom_histogram(aes(x = rt), bins = 100) +
  facet_wrap(~ageGroup) +
  scale_x_log10(breaks = seq(0, 1000, 100)) +
  geom_vline(xintercept = 250, color = "red")

d2 <- d1 %>%
  filter(!(rt < 250) | is.na(rt)) # preset criterion of 250ms
```

## Look at hover time

```{r hover time}
d2 %>%
  filter(!(capacity %in% c("personality", "beliefs", "pleasure", "desires", "self_restraint", "goals", "nauseated"))) %>%
  mutate(noHover = hoverTime == 0) %>%
  count(ageGroup, noHover) %>%
  group_by(ageGroup) %>%
  mutate(percent = n/sum(n))

ggplot(d2, aes(x = hoverTime)) +
  facet_grid(~ ageGroup) +
  geom_histogram()
```

## Prepare data for EFA

```{r prep for efa}
# finish tidying
d3 <- d2 %>%
  select(capacity, responseNum, subid) %>%
  spread(capacity, responseNum)

# make combined dataset
d3_combined <- data.frame(d3[,-1], row.names = d3[,1])

# make separate child and adult datasets
d3_adult <- d3 %>%
  left_join(d %>% select(subid, ageGroup)) %>%
  filter(ageGroup == "adult") %>%
  select(-ageGroup) %>%
  distinct()
d3_adult <- data.frame(d3_adult[,-1], row.names = d3_adult[,1])

d3_child <- d3 %>%
  left_join(d %>% select(subid, ageGroup)) %>%
  filter(ageGroup == "child") %>%
  select(-ageGroup) %>%
  distinct()
d3_child <- data.frame(d3_child[,-1], row.names = d3_child[,1])
```

## General analysis settings

```{r analysis settings}
# set correlation type: pearson or polychoric?
cor_type <- "cor"
# cor_type <- "poly"

# set rotation type: varimax or oblimin?
rot_type <- "varimax"
# rot_type <- "oblimin"

# set score type
# score_type <- "regression"
# score_type <- "Thurstone"
score_type <- "tenBerge"
# score_type <- "Anderson"
# score_type <- "Bartlett"
# score_type <- "Harman"
```

# Adults alone

## Exploratory factor analysis

### Maximal (13-factor) unrotated solution

```{r efa adults maximal unrotated}
# do factor analysis
efa_adult_max_unrot <- fa(d3_adult, nfactors = 13, rotate = "none", cor = cor_type)
efa_adult_max_unrot

# examine eigenvalues and variance explained
efa_adult_max_unrot_eigenvalues <- print(efa_adult_max_unrot)$Vaccounted %>%
  t() %>%
  data.frame()

# count factors with eigenvalues > 1 and variance explained > 5%
efa_adult_max_unrot_nfactors <- efa_adult_max_unrot_eigenvalues %>%
  filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
  count() %>%
  as.numeric()
efa_adult_max_unrot_nfactors

# manually check that each factor is the dominant factor for at least one mental capacity item
efa_adult_max_unrot_loadings <- fa.sort(loadings(efa_adult_max_unrot)[]) %>%
  data.frame() %>%
  select(1:efa_adult_max_unrot_nfactors) %>%
  rename(F1 = MR1, F2 = MR2, F3 = MR3, F4 = MR4) %>% # adjust by hand as needed
  mutate(F1_abs = abs(F1),
         F2_abs = abs(F2),
         F3_abs = abs(F3),
         F4_abs = abs(F4),
         loading_abs = pmax(F1_abs, F2_abs, F3_abs, F4_abs),
         loading = ifelse(loading_abs == abs(F1), F1,
                          ifelse(loading_abs == abs(F2), F2,
                                 ifelse(loading_abs == abs(F3), F3,
                                        ifelse(loading_abs == abs(F4), F4,
                                               NA)))),
         factor = ifelse(loading == F1, "F1",
                         ifelse(loading == F2, "F2",
                                ifelse(loading == F3, "F3",
                                       ifelse(loading == F4, "F4",
                                              NA)))),
         factorName = ifelse(loading == F1, "Factor 1",
                             ifelse(loading == F2, "Factor 2",
                                    ifelse(loading == F3, "Factor 3",
                                           ifelse(loading == F4, "Factor 4",
                                                  NA)))))

efa_adult_max_unrot_loadings %>% count(factorName) # drop any factors where n < 1

# reset as needed
efa_adult_max_unrot_nfactors <- efa_adult_max_unrot_loadings %>% count(factorName) %>% nrow()
```

### Maximal (13-factor) rotated solution

```{r efa adults maximal rotated}
# do factor analysis
efa_adult_max_rot <- fa(d3_adult, nfactors = 13, rotate = rot_type, cor = cor_type)
efa_adult_max_rot

# examine eigenvalues and variance explained
efa_adult_max_rot_eigenvalues <- print(efa_adult_max_rot)$Vaccounted %>%
  t() %>%
  data.frame()
efa_adult_max_rot_eigenvalues

# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"
```

### Small rotated solution

```{r efa adults small rotated}
# do factor analysis
efa_adult_small_rot <- fa(d3_adult, nfactors = efa_adult_max_unrot_nfactors, rotate = rot_type, cor = cor_type)
efa_adult_small_rot

# examine eigenvalues and variance explained
efa_adult_small_rot_eigenvalues <- print(efa_adult_small_rot)$Vaccounted %>%
  t() %>%
  data.frame()
efa_adult_small_rot_eigenvalues

# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"
```

#### Loadings

```{r efa adults small rotated loadings}
# make dataframe for all factor loadings and dominant factor
efa_adult_small_rot_loadings <- efa_adult_small_rot$loadings[] %>%
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column(var = "capacity") %>%
  rename(F1 = MR1, F2 = MR2, F3 = MR3) %>% # adjust by hand as needed
  mutate(F1_abs = abs(F1),
         F2_abs = abs(F2),
         F3_abs = abs(F3),
         loading_abs = pmax(F1_abs, F2_abs, F3_abs),
         loading = ifelse(loading_abs == abs(F1), F1,
                          ifelse(loading_abs == abs(F2), F2,
                                 ifelse(loading_abs == abs(F3), F3,
                                        NA))),
         factor = ifelse(loading == F1, "F1",
                         ifelse(loading == F2, "F2",
                                ifelse(loading == F3, "F3",
                                       NA))),
         factorName = ifelse(loading == F1, "Factor 1",
                             ifelse(loading == F2, "Factor 2",
                                    ifelse(loading == F3, "Factor 3",
                                           NA)))) %>%
  select(capacity, F1, F2, F3, factor, factorName, loading, loading_abs) %>%
  distinct() %>%
  full_join(d %>% select(capacity, capWording)) %>%
  mutate(capWording = gsub("\\ ", "_", gsub(" --.*", "", capWording))) %>%
  select(capacity, capWording, F1:loading_abs) %>%
  distinct()

# print out list of items by dominant factor
efa_adult_small_rot_items <- efa_adult_small_rot_loadings %>%
  arrange(factor, desc(loading_abs)) %>%
  mutate(capWordingPlus = ifelse(loading > 0, capWording, 
                                 paste0(capWording, " (negative loading)"))) %>%
  group_by(factor) %>%
  summarise(items = gsub("_", " ", paste(capWordingPlus, collapse = ", ")))
head(efa_adult_small_rot_items$items, 40)
```

# Children alone

## Exploratory factor analysis

### Maximal (13-factor) unrotated solution

```{r efa children maximal unrotated}
# do factor analysis
efa_child_max_unrot <- fa(d3_child, nfactors = 13, rotate = "none", cor = cor_type)
efa_child_max_unrot

# examine eigenvalues and variance explained
efa_child_max_unrot_eigenvalues <- print(efa_child_max_unrot)$Vaccounted %>%
  t() %>%
  data.frame()

# count factors with eigenvalues > 1 and variance explained > 5%
efa_child_max_unrot_nfactors <- efa_child_max_unrot_eigenvalues %>%
  filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
  count() %>%
  as.numeric()
efa_child_max_unrot_nfactors

# manually check that each factor is the dominant factor for at least one mental capacity item
efa_child_max_unrot_loadings <- fa.sort(loadings(efa_child_max_unrot)[]) %>%
  data.frame() %>%
  select(1:efa_child_max_unrot_nfactors) %>%
  rename(F1 = MR1, F2 = MR2, F3 = MR3) %>% # adjust by hand as needed
  mutate(F1_abs = abs(F1),
         F2_abs = abs(F2),
         F3_abs = abs(F3),
         loading_abs = pmax(F1_abs, F2_abs, F3_abs),
         loading = ifelse(loading_abs == abs(F1), F1,
                          ifelse(loading_abs == abs(F2), F2,
                                 ifelse(loading_abs == abs(F3), F3,
                                        NA))),
         factor = ifelse(loading == F1, "F1",
                         ifelse(loading == F2, "F2",
                                ifelse(loading == F3, "F3",
                                       NA))),
         factorName = ifelse(loading == F1, "Factor 1",
                             ifelse(loading == F2, "Factor 2",
                                    ifelse(loading == F3, "Factor 3",
                                           NA))))

efa_child_max_unrot_loadings %>% count(factorName) # drop any factors where n < 1

# reset as needed
efa_child_max_unrot_nfactors <- efa_child_max_unrot_loadings %>% count(factorName) %>% nrow()
```

### Maximal (13-factor) rotated solution

```{r efa children maximal rotated}
# do factor analysis
efa_child_max_rot <- fa(d3_child, nfactors = 13, rotate = rot_type, cor = cor_type)
efa_child_max_rot

# examine eigenvalues and variance explained
efa_child_max_rot_eigenvalues <- print(efa_child_max_rot)$Vaccounted %>%
  t() %>%
  data.frame()
efa_child_max_rot_eigenvalues

# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"
```

### Small rotated solution

```{r efa children small rotated}
# do factor analysis
efa_child_small_rot <- fa(d3_child, nfactors = efa_child_max_unrot_nfactors, rotate = rot_type, cor = cor_type)
efa_child_small_rot

# examine eigenvalues and variance explained
efa_child_small_rot_eigenvalues <- print(efa_child_small_rot)$Vaccounted %>%
  t() %>%
  data.frame()
efa_child_small_rot_eigenvalues

# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"
```

#### Loadings

```{r efa children small rotated loadings}
# make dataframe for all factor loadings and dominant factor
efa_child_small_rot_loadings <- efa_child_small_rot$loadings[] %>%
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column(var = "capacity") %>%
  rename(F1 = MR1, F2 = MR2, F3 = MR3) %>% # adjust by hand as needed
  mutate(F1_abs = abs(F1),
         F2_abs = abs(F2),
         F3_abs = abs(F3),
         loading_abs = pmax(F1_abs, F2_abs, F3_abs),
         loading = ifelse(loading_abs == abs(F1), F1,
                          ifelse(loading_abs == abs(F2), F2,
                                 ifelse(loading_abs == abs(F3), F3,
                                        NA))),
         factor = ifelse(loading == F1, "F1",
                         ifelse(loading == F2, "F2",
                                ifelse(loading == F3, "F3",
                                       NA))),
         factorName = ifelse(loading == F1, "Factor 1",
                             ifelse(loading == F2, "Factor 2",
                                    ifelse(loading == F3, "Factor 3",
                                           NA)))) %>%
  select(capacity, F1, F2, F3, factor, factorName, loading, loading_abs) %>%
  distinct() %>%
  full_join(d %>% select(capacity, capWording)) %>%
  mutate(capWording = gsub("\\ ", "_", gsub(" --.*", "", capWording))) %>%
  select(capacity, capWording, F1:loading_abs) %>%
  distinct()

# print out list of items by dominant factor
efa_child_small_rot_items <- efa_child_small_rot_loadings %>%
  arrange(factor, desc(loading_abs)) %>%
  mutate(capWordingPlus = ifelse(loading > 0, capWording, 
                                 paste0(capWording, " (negative loading)"))) %>%
  group_by(factor) %>%
  summarise(items = gsub("_", " ", paste(capWordingPlus, collapse = ", ")))
head(efa_child_small_rot_items$items, 40)
```

# Combined dataset: Children & adults

## Exploratory factor analysis

### Maximal (13-factor) unrotated solution

```{r efa combined maximal unrotated}
# do factor analysis
efa_comb_max_unrot <- fa(d3_combined, nfactors = 13, rotate = "none", cor = cor_type)
efa_comb_max_unrot

# examine eigenvalues and variance explained
efa_comb_max_unrot_eigenvalues <- print(efa_comb_max_unrot)$Vaccounted %>%
  t() %>%
  data.frame()

# count factors with eigenvalues > 1 and variance explained > 5%
efa_comb_max_unrot_nfactors <- efa_comb_max_unrot_eigenvalues %>%
  filter(SS.loadings > 1, Proportion.Explained > 0.05) %>%
  count() %>%
  as.numeric()
efa_comb_max_unrot_nfactors

# manually check that each factor is the dominant factor for at least one mental capacity item
efa_comb_max_unrot_loadings <- fa.sort(loadings(efa_comb_max_unrot)[]) %>%
  data.frame() %>%
  select(1:efa_comb_max_unrot_nfactors) %>%
  rename(F1 = MR1, F2 = MR2, F3 = MR3) %>% # adjust by hand as needed
  mutate(F1_abs = abs(F1),
         F2_abs = abs(F2),
         F3_abs = abs(F3),
         loading_abs = pmax(F1_abs, F2_abs, F3_abs),
         loading = ifelse(loading_abs == abs(F1), F1,
                          ifelse(loading_abs == abs(F2), F2,
                                 ifelse(loading_abs == abs(F3), F3,
                                        NA))),
         factor = ifelse(loading == F1, "F1",
                         ifelse(loading == F2, "F2",
                                ifelse(loading == F3, "F3",
                                       NA))),
         factorName = ifelse(loading == F1, "Factor 1",
                             ifelse(loading == F2, "Factor 2",
                                    ifelse(loading == F3, "Factor 3",
                                           NA))))

efa_comb_max_unrot_loadings %>% count(factorName) # drop any factors where n < 1
```

### Maximal (13-factor) rotated solution

```{r efa combined maximal rotated}
# do factor analysis
efa_comb_max_rot <- fa(d3_combined, nfactors = 13, rotate = rot_type, cor = cor_type)
efa_comb_max_rot

# examine eigenvalues and variance explained
efa_comb_max_rot_eigenvalues <- print(efa_comb_max_rot)$Vaccounted %>%
  t() %>%
  data.frame()
efa_comb_max_rot_eigenvalues

# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"
```

### Small rotated solution

```{r efa combined small rotated}
# do factor analysis
efa_comb_small_rot <- fa(d3_combined, nfactors = efa_comb_max_unrot_nfactors, rotate = rot_type, cor = cor_type)
efa_comb_small_rot

# examine eigenvalues and variance explained
efa_comb_small_rot_eigenvalues <- print(efa_comb_small_rot)$Vaccounted %>%
  t() %>%
  data.frame()
efa_comb_small_rot_eigenvalues

# but see https://www.researchgate.net/post/How_to_calculate_the_explained_variance_per_factor_in_a_principal_axis_factor_analysis if rotation != "varimax"
# takeaway: use "Proportion.Var" instead of "Proportion.Explained"
```

#### Loadings

```{r efa combined small rotated loadings}
# make dataframe for all factor loadings and dominant factor
efa_comb_small_rot_loadings <- efa_comb_small_rot$loadings[] %>%
  fa.sort() %>%
  data.frame() %>%
  rownames_to_column(var = "capacity") %>%
  rename(F1 = MR1, F2 = MR2, F3 = MR3) %>% # adjust by hand as needed
  mutate(F1_abs = abs(F1),
         F2_abs = abs(F2),
         F3_abs = abs(F3),
         loading_abs = pmax(F1_abs, F2_abs, F3_abs),
         loading = ifelse(loading_abs == abs(F1), F1,
                          ifelse(loading_abs == abs(F2), F2,
                                 ifelse(loading_abs == abs(F3), F3,
                                        NA))),
         factor = ifelse(loading == F1, "F1",
                         ifelse(loading == F2, "F2",
                                ifelse(loading == F3, "F3",
                                       NA))),
         factorName = ifelse(loading == F1, "Factor 1",
                             ifelse(loading == F2, "Factor 2",
                                    ifelse(loading == F3, "Factor 3",
                                           NA)))) %>%
  select(capacity, F1, F2, F3, factor, factorName, loading, loading_abs) %>%
  distinct() %>%
  full_join(d %>% select(capacity, capWording)) %>%
  mutate(capWording = gsub("\\ ", "_", gsub(" --.*", "", capWording))) %>%
  select(capacity, capWording, F1:loading_abs) %>%
  distinct()

# print out list of items by dominant factor
efa_comb_small_rot_items <- efa_comb_small_rot_loadings %>%
  arrange(factor, desc(loading_abs)) %>%
  mutate(capWordingPlus = ifelse(loading > 0, capWording, 
                                 paste0(capWording, " (negative loading)"))) %>%
  group_by(factor) %>%
  summarise(items = gsub("_", " ", paste(capWordingPlus, collapse = ", ")))
head(efa_comb_small_rot_items$items, 40)
```

# Regression on factor scores

## Children vs. adults

```{r regression age group data prep}
scores_all <- fa(d3_combined, nfactors = efa_comb_max_unrot_nfactors, rotate = rot_type,
                 cor = cor_type, scores = score_type)$scores %>%
  data.frame() %>%
  rownames_to_column(var = "subid") %>%
  mutate(ageGroup = factor(ifelse(grepl("run", subid), "adult", "child")))

colnames(scores_all)[2:4] <- c("score_F1", "score_F2", "score_F3")
```

```{r regression age group analysis}
# analyze
scores_all_analysis <- d %>%
  select(subid, ageGroup, character) %>%
  distinct() %>%
  left_join(scores_all) %>%
  mutate(character = factor(character)) %>%
  filter(!is.na(score_F1) & !is.na(score_F2) & !is.na(score_F3), !is.na(ageGroup)) %>%
  gather(factor, score, starts_with("score_")) %>%
  mutate(factor = factor(factor))

# set contrasts
contrasts(scores_all_analysis$factor) <- cbind(F1 = c(1, -1, 0), # MAKE SURE TO DOUBLE-CHECK!!
                                               F3 = c(0, -1, 1))
contrasts(scores_all_analysis$character) <- cbind(robot = c(-1, 1))
contrasts(scores_all_analysis$ageGroup) <- cbind(child = c(-1, 1))

r1 <- lmer(score ~ character * factor + (1 | subid), scores_all_analysis)
r2 <- lmer(score ~ character * factor + ageGroup + (1 | subid), scores_all_analysis)
r3 <- lmer(score ~ character * factor * ageGroup + (1 | subid), scores_all_analysis)
anova(r1, r2, r3)
# summary(r1)
# summary(r2)
summary(r3)

round(signif(summary(r3)$coefficients, 3), 2) %>% data.frame() # %>% View()

# stepwise regression
drop1(r3, test = "Chisq")
r3_step2 <- lmer(score ~ character * factor + ageGroup + character:ageGroup + factor:ageGroup + (1 | subid), scores_all_analysis)
drop1(r3_step2, test = "Chisq")
r3_step3 <- lmer(score ~ character * factor + ageGroup + factor:ageGroup + (1 | subid), scores_all_analysis)
drop1(r3_step3, test = "Chisq")

# # robot only
# robot_r1 <- lmer(score ~ factor + (1 | subid), scores_all_analysis %>% filter(character == "robot"))
# robot_r2 <- lmer(score ~ factor + ageGroup + (1 | subid), scores_all_analysis %>% filter(character == "robot"))
# robot_r3 <- lmer(score ~ factor * ageGroup + (1 | subid), scores_all_analysis %>% filter(character == "robot"))
# anova(robot_r1, robot_r2, robot_r3)
# # summary(robot_r1)
# # summary(robot_r2)
# summary(robot_r3)
```

```{r regression age group plot}
scores_all_plotting <- d %>%
  select(subid, ageGroup, character) %>%
  distinct() %>%
  full_join(scores_all) %>%
  mutate(character = factor(character)) %>%
  filter(!is.na(score_F1), !is.na(score_F2), !is.na(score_F3), !is.na(ageGroup)) %>%
  gather(factor, score, starts_with("score_")) %>%
  mutate(factor = factor(factor)) %>%
  multi_boot(column = "score",
             summary_groups = c("ageGroup", "character", "factor"),
             statistics_functions = c("mean", "ci_lower", "ci_upper"))

# plot
ggplot(scores_all_plotting %>%
         ungroup() %>%
         mutate(factor = factor(factor,
                                labels = c("Social-emotional",
                                           "Physiological",
                                           "Perceptual-cognitive")),
                ageGroup = factor(ageGroup,
                                  levels = c("adult", "child"),
                                  labels = c("adults", "children"))),
                                  # levels = c("child", "adult"),
                                  # labels = c("children", "adults"))),
       aes(x = ageGroup, y = mean, color = character, shape = character)) +
  facet_wrap("factor", ncol = 3) +
  theme_bw() +
  theme(text = element_text(size = 28),
        legend.position = "bottom") +
  geom_point(size = 5, position = position_dodge(width = 0.4)) +
  geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper), 
                width = 0.2, position = position_dodge(width = 0.4)) +
  scale_shape_manual(values = c(19, 15)) +
  labs(title = "Factor scores by age group",
       # subtitle = "Adults (Study 1) vs. children (Study 2)\n",
       x = "Age group",
       y = "Mean factor score") # 1000 by 500
```

## Children by age at test

```{r regression age at test data prep}
scores_children <- fa(d3_child, nfactors = efa_comb_max_unrot_nfactors, rotate = rot_type,
                 cor = cor_type, scores = score_type)$scores %>%
  data.frame() %>%
  rownames_to_column(var = "subid") %>%
  mutate(ageGroup = factor(ifelse(grepl("run", subid), "adult", "child")))

colnames(scores_children)[2:4] <- c("score_F1", "score_F2", "score_F3")
```

```{r regression age at test analysis}
# analyze
scores_children_analysis <- d %>%
  select(subid, age, character) %>%
  distinct() %>%
  left_join(scores_children) %>%
  mutate(character = factor(character)) %>%
  filter(!is.na(score_F1) & !is.na(score_F2) & !is.na(score_F3), !is.na(ageGroup)) %>%
  gather(factor, score, starts_with("score_")) %>%
  mutate(factor = factor(factor))

# set contrasts
contrasts(scores_children_analysis$factor) <- cbind(F1 = c(1, -1, 0), # MAKE SURE TO DOUBLE-CHECK!!
                                               F3 = c(0, -1, 1))
contrasts(scores_children_analysis$character) <- cbind(robot = c(-1, 1))

r4 <- lmer(score ~ character * factor + (1 | subid), scores_children_analysis)
r5 <- lmer(score ~ character * factor + scale(age) + (1 | subid), scores_children_analysis)
r6 <- lmer(score ~ character * factor * scale(age) + (1 | subid), scores_children_analysis)
anova(r4, r5, r6)
# summary(r4)
# summary(r5)
summary(r6)

# stepwise regression
drop1(r6, test = "Chisq")
r6_step2 <- lmer(score ~ character * factor + scale(age) + character:scale(age) + factor:scale(age) + (1 | subid), scores_children_analysis)
drop1(r6_step2, test = "Chisq")
summary(r6_step2)

# explore polynoial effects of age
r6b <- lmer(score ~ character * factor * poly(age, 1) + (1 | subid), scores_children_analysis)
r7 <- lmer(score ~ character * factor * poly(age, 2) + (1 | subid), scores_children_analysis)
r8 <- lmer(score ~ character * factor * poly(age, 3) + (1 | subid), scores_children_analysis)
anova(r6b, r7, r8)
# summary(r6b)
# summary(r7)
summary(r8)

round(signif(summary(r6)$coefficients, 3), 2) %>% data.frame() # %>% View()

# stepwise regression
drop1(r8, test = "Chisq")
r8_step2 <- lmer(score ~ character * factor * poly(age, 2) + poly(age, 3) + poly(age, 3):character + poly(age, 3):factor + (1 | subid), scores_children_analysis)
drop1(r8_step2, test = "Chisq")
r8_step3 <- lmer(score ~ character * factor + poly(age, 3) + (1 | subid) + poly(age, 2) + character:poly(age, 2) + factor:poly(age, 2), scores_children_analysis)
drop1(r8_step3, test = "Chisq")
summary(r8_step3)

# # robot only
# robot_r4 <- lmer(score ~ factor + (1 | subid), scores_children_analysis %>% filter(character == "robot"))
# robot_r5 <- lmer(score ~ factor + scale(age) + (1 | subid), scores_children_analysis %>% filter(character == "robot"))
# robot_r6 <- lmer(score ~ factor * scale(age) + (1 | subid), scores_children_analysis %>% filter(character == "robot"))
# anova(robot_r4, robot_r5, robot_r6)
# # summary(robot_r4)
# # summary(robot_r5)
# summary(robot_r6)
```

```{r regression age at test plot}
scores_children_plotting <- d %>%
  select(subid, age, character) %>%
  distinct() %>%
  full_join(scores_children) %>%
  mutate(character = factor(character)) %>%
  filter(!is.na(score_F1), !is.na(score_F2), !is.na(score_F3), !is.na(age)) %>%
  gather(factor, score, starts_with("score_")) %>%
  mutate(factor = factor(factor)) %>%
  multi_boot(column = "score",
             summary_groups = c("age", "character", "factor"),
             statistics_functions = c("mean", "ci_lower", "ci_upper"))

# plot
ggplot(scores_children_plotting %>%
         ungroup() %>%
         mutate(factor = factor(factor,
                                labels = c("Social-emotional",
                                           "Physiological",
                                           "Perceptual-cognitive"))),
       aes(x = age, y = mean, color = character, fill = character, shape = character)) +
  facet_wrap("factor", ncol = 3) +
  theme_bw() +
  theme(text = element_text(size = 28),
        legend.position = "bottom") +
  # geom_smooth(method = "loess", alpha = 0.4) +
  geom_smooth(method = "lm", alpha = 0.4) +
  geom_point(size = 2) +
  scale_shape_manual(values = c(19, 15)) +
  labs(title = "Factor scores by children's age",
       # subtitle = "Children (Study 2)\n",
       x = "Age (years)",
       y = "Factor score") # 1000 by 500
```

## Adults by age at test

```{r regression adult age at test data prep}
scores_adults <- fa(d3_adult, nfactors = efa_comb_max_unrot_nfactors, rotate = rot_type,
                 cor = cor_type, scores = score_type)$scores %>%
  data.frame() %>%
  rownames_to_column(var = "subid") %>%
  mutate(ageGroup = factor(ifelse(grepl("run", subid), "adult", "adult")))

colnames(scores_adults)[2:4] <- c("score_F1", "score_F2", "score_F3")
```

```{r regression adult age at test analysis}
# analyze
scores_adults_analysis <- d %>%
  select(subid, age, character) %>%
  distinct() %>%
  left_join(scores_adults) %>%
  mutate(character = factor(character)) %>%
  filter(!is.na(score_F1) & !is.na(score_F2) & !is.na(score_F3), !is.na(age)) %>%
  gather(factor, score, starts_with("score_")) %>%
  mutate(factor = factor(factor))

# set contrasts
contrasts(scores_adults_analysis$factor) <- cbind(F1 = c(1, -1, 0), # MAKE SURE TO DOUBLE-CHECK!!
                                               F3 = c(0, -1, 1))
contrasts(scores_adults_analysis$character) <- cbind(robot = c(-1, 1))

r4 <- lmer(score ~ character * factor + (1 | subid), scores_adults_analysis)
r5 <- lmer(score ~ character * factor + scale(age) + (1 | subid), scores_adults_analysis)
r6 <- lmer(score ~ character * factor * scale(age) + (1 | subid), scores_adults_analysis)
anova(r4, r5, r6)
# summary(r4)
# summary(r5)
summary(r6)

# stepwise regression
drop1(r6, test = "Chisq")
r6_step2 <- lmer(score ~ character * factor + scale(age) + character:scale(age) + factor:scale(age) + (1 | subid), scores_adults_analysis)
drop1(r6_step2, test = "Chisq")
summary(r6_step2)

# explore polynoial effects of age
r6b <- lmer(score ~ character * factor * poly(age, 1) + (1 | subid), scores_adults_analysis)
r7 <- lmer(score ~ character * factor * poly(age, 2) + (1 | subid), scores_adults_analysis)
r8 <- lmer(score ~ character * factor * poly(age, 3) + (1 | subid), scores_adults_analysis)
anova(r6b, r7, r8)
# summary(r6b)
summary(r7)
summary(r8)

round(signif(summary(r6)$coefficients, 3), 2) %>% data.frame() # %>% View()

# stepwise regression
drop1(r8, test = "Chisq")
r8_step2 <- lmer(score ~ character * factor * poly(age, 2) + poly(age, 3) + poly(age, 3):character + poly(age, 3):factor + (1 | subid), scores_adults_analysis)
drop1(r8_step2, test = "Chisq")
r8_step3 <- lmer(score ~ character * factor + poly(age, 3) + (1 | subid) + poly(age, 2) + character:poly(age, 2) + factor:poly(age, 2), scores_adults_analysis)
drop1(r8_step3, test = "Chisq")
summary(r8_step3)
```

```{r regression adult age at test plot}
scores_adults_plotting <- d %>%
  filter(!is.na(age)) %>%
  select(subid, age, character) %>%
  distinct() %>%
  full_join(scores_adults) %>%
  mutate(character = factor(character)) %>%
  filter(!is.na(score_F1), !is.na(score_F2), !is.na(score_F3), !is.na(age)) %>%
  gather(factor, score, starts_with("score_")) %>%
  mutate(factor = factor(factor)) %>%
  multi_boot(column = "score",
             summary_groups = c("age", "character", "factor"),
             statistics_functions = c("mean", "ci_lower", "ci_upper"))

# plot
ggplot(scores_adults_plotting %>%
         ungroup() %>%
         mutate(factor = factor(factor,
                                labels = c("Social-emotional",
                                           "Physiological",
                                           "Perceptual-cognitive"))),
       aes(x = age, y = mean, color = character, fill = character, shape = character)) +
  facet_wrap("factor", ncol = 3) +
  theme_bw() +
  theme(text = element_text(size = 28),
        legend.position = "bottom") +
  # geom_smooth(method = "loess", alpha = 0.4) +
  geom_smooth(method = "lm", alpha = 0.4) +
  geom_point(size = 2) +
  scale_shape_manual(values = c(19, 15)) +
  labs(title = "Factor scores by adults's age",
       # subtitle = "Adults (Study 2)\n",
       x = "Age (years)",
       y = "Factor score") # 1000 by 500
```

## Big figure

```{r big figure}
# by condition
d1_bycond2 <- d2 %>%
  left_join(d_child02 %>% select(subid, age)) %>%
  select(character, capacity, capWording, responseNum, subid, ageGroup, age) %>%
  filter(capacity != "na", is.na(responseNum) == F) %>%
  mutate(capWording = gsub(" --.*", "", capWording),
         ageGroup3 = ifelse(ageGroup == "adult", "adult",
                            ifelse(is.na(age), NA,
                                   ifelse(age < 8, "7y", 
                                          ifelse(age < 9, "8y",
                                                 ifelse(age < 10, "9y",
                                                        NA)))))) %>%
  distinct()

d1_bycond2 %>% select(ageGroup3, subid) %>% distinct() %>% count(ageGroup3)

# make df for plotting
d1_bycond2_mb <- multi_boot(d1_bycond2,
                           column = "responseNum",
                           summary_groups = c("ageGroup3", "character", "capacity", "capWording"),
                           statistics_functions = c("mean", "ci_lower", "ci_upper"))

d1_bycond2_mb_factorsAll <- d1_bycond2_mb %>% 
  full_join(efa_comb_small_rot_loadings %>%
              group_by(factor) %>%
              mutate(order = rank(desc(loading_abs)))) %>%
  arrange(character, factor, desc(loading_abs)) %>%
  rownames_to_column(var = "full_order") %>%
  mutate(full_order = as.numeric(full_order)) %>%
  arrange(factorName, full_order) %>%
  ungroup() %>%
  filter(!is.na(ageGroup3)) %>%
  mutate(factorName = factor(factorName,
                             levels = c("Factor 1", "Factor 2", "Factor 3"),
                             labels = c("Social-emotional", 
                                        "Physiological", 
                                        "Perceptual-cognitive")))

dodge_width <- 2

ggplot(d1_bycond2_mb_factorsAll, 
       aes(x = desc(order*2), y = mean,
           color = ageGroup3, shape = ageGroup3,
           label = capWording)) +
  facet_grid(factorName ~ character, scales = "free", space = "free") +
  geom_hline(yintercept = 0, lty = 3) +
  geom_hline(yintercept = 0.5, lty = 3) +
  geom_hline(yintercept = 1, lty = 3) +
  geom_point(stat = "identity", position = position_dodge(width = dodge_width), size = 8) +
  scale_shape_manual(values = c(rep(18, 3), 17)) +
  # scale_colour_brewer(type = "seq", palette = "PuRd") +
  scale_colour_hue(h = c(0, 180)) +
  geom_errorbar(aes(ymin = ci_lower, ymax = ci_upper),
                position = position_dodge(width = dodge_width), width = 0) +
  geom_text(aes(y = -0.5, hjust = 0), 
            color = "black",
            # color = d1_bycond2_mb_factorsAll$textColor,
            size = 8) +
  labs(title = "Responses by mental capacity item",
       y = "Mean response (0 = NO, 0.5 = KINDA, 1 = YES)",
       x = "Capacity",
       color = "Age group: ", shape = "Age group: ") +
  scale_y_continuous(breaks = c(0, 0.5, 1)) +
  coord_flip() +
  theme_bw() +
  theme(text = element_text(size = 28),
        # axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        legend.position = "top") # 1700 by 2000

```

```{r tables and chisq tests for items}
# FILTERED BY ROBOT

# feel safe
with(d2 %>% filter(capWording == "feel_safe", 
                   !is.na(responseCat),
                   character == "robot"),
     table(ageGroup, responseCat)) #%>%
  #summary()

# feel tired
with(d2 %>% filter(capWording == "feel_tired", 
                   !is.na(responseCat),
                   character == "robot"),
     table(ageGroup, responseCat)) #%>%
  #summary()

# feel scared
with(d2 %>% filter(capWording == "feel_scared", 
                   !is.na(responseCat),
                   character == "robot"),
     table(ageGroup, responseCat)) #%>%
  #summary()

# get hungry
with(d2 %>% filter(capWording == "get_hungry", 
                   !is.na(responseCat),
                   character == "robot"),
     table(ageGroup, responseCat)) #%>%
  #summary()

# feel pain
with(d2 %>% filter(capWording == "feel_pain", 
                   !is.na(responseCat),
                   character == "robot"),
     table(ageGroup, responseCat)) #%>%
  #summary()

# feel proud
proud_table <- with(d2 %>% filter(capWording == "feel_proud", 
                   !is.na(responseCat),
                   character == "robot") %>%
       mutate(ageGroup3 = ifelse(ageGroup == "adult", "adult",
                                 ifelse(is.na(age), NA,
                                        ifelse(age < 8, "7",
                                               ifelse(age < 9, "8",
                                                      "9"))))),
     table(ageGroup3, responseCat)) %>%
  prop.table(1) 
proud_table
proud_table[3,2]+proud_table[3,3]

# feel happy
happy_table <- with(d2 %>% filter(capWording == "feel_happy", 
                   !is.na(responseCat),
                   character == "robot") %>%
       mutate(ageGroup3 = ifelse(ageGroup == "adult", "adult",
                                 ifelse(is.na(age), NA,
                                        ifelse(age < 8, "7",
                                               ifelse(age < 9, "8",
                                                      "9"))))),
     table(ageGroup3, responseCat)) %>%
  prop.table(1) 
happy_table
happy_table[3,2]+happy_table[3,3]

# feel guilty
guilty_table <- with(d2 %>% filter(capWording == "feel_guilty", 
                   !is.na(responseCat),
                   character == "robot") %>%
       mutate(ageGroup3 = ifelse(ageGroup == "adult", "adult",
                                 ifelse(is.na(age), NA,
                                        ifelse(age < 8, "7",
                                               ifelse(age < 9, "8",
                                                      "9"))))),
     table(ageGroup3, responseCat)) %>%
  prop.table(1) 
guilty_table
guilty_table[1,2]+guilty_table[1,3]
guilty_table[2,2]+guilty_table[2,3]
guilty_table[3,2]+guilty_table[3,3]

clm_guilty <- ordinal::clm(responseCat ~ age,
                           data = d2 %>% 
                             filter(capWording == "feel_guilty",
                                    !is.na(responseCat),
                                    character == "robot"))
summary(clm_guilty)

# personality
personality_table <- with(d2 %>% filter(capWording == "have_a_personality", 
                   !is.na(responseCat),
                   character == "robot") %>%
       mutate(ageGroup3 = ifelse(ageGroup == "adult", "adult",
                                 ifelse(is.na(age), NA,
                                        ifelse(age < 8, "7",
                                               ifelse(age < 9, "8",
                                                      "9"))))),
     table(ageGroup3, responseCat)) %>%
  prop.table(1) 
personality_table
personality_table[3,2]+personality_table[3,3]

# self-control
self_control_table <- with(d2 %>% filter(capWording == "have_self-control", 
                   !is.na(responseCat),
                   character == "robot") %>%
       mutate(ageGroup3 = ifelse(ageGroup == "adult", "adult",
                                 ifelse(is.na(age), NA,
                                        ifelse(age < 8, "7",
                                               ifelse(age < 9, "8",
                                                      "9"))))),
     table(ageGroup3, responseCat)) %>%
  prop.table(1) 
self_control_table
self_control_table[1,2]+self_control_table[1,3]
self_control_table[2,2]+self_control_table[2,3]
self_control_table[3,2]+self_control_table[3,3]

clm_self_control <- ordinal::clm(responseCat ~ age,
                                 data = d2 %>% 
                                   filter(capWording == "have_self-control",
                                          !is.na(responseCat),
                                          character == "robot"))
summary(clm_self_control)

# MORE GENERAL

at_least_kinda_9y <- d2 %>% 
  filter(!is.na(responseCat),
         ageGroup == "child",
         !is.na(age),
         age > 9) %>%
  distinct() %>%
  count(character, capacity, responseCat) %>%
  group_by(character, capacity) %>%
  mutate(prop_n = n/sum(n, na.rm = T)) %>%
  select(-n) %>%
  spread(responseCat, prop_n) %>% # n) %>%
  mutate(at_least_kinda = sum(kinda, yes)) %>%
  left_join(efa_comb_small_rot_loadings) %>%
  arrange(factor, desc(at_least_kinda))

at_least_kinda_9y %>%
  select(character:at_least_kinda, factor, loading)

at_least_kinda_adult <- d2 %>% 
  filter(!is.na(responseCat),
         character == "robot",
         ageGroup == "adult") %>%
  distinct() %>%
  count(capacity, responseCat) %>%
  group_by(capacity) %>%
  mutate(prop_n = n/sum(n, na.rm = T)) %>%
  select(-n) %>%
  spread(responseCat, prop_n) %>% # n) %>%
  mutate(at_least_kinda = sum(kinda, yes)) %>%
  left_join(efa_comb_small_rot_loadings) %>%
  arrange(factor, desc(at_least_kinda))

at_least_kinda_adult %>%
  select(capacity:at_least_kinda, factor, loading)

```

## BIG TABLE

```{r big table}
# make dataframe
big_table <- efa_adult_small_rot_loadings %>% 
  setNames(paste0('adult_', names(.))) %>% 
  rename(capacity = adult_capacity, 
         capWording = adult_capWording) %>%
  full_join(efa_child_small_rot_loadings %>%
              setNames(paste0('child_', names(.))) %>%
              rename(capacity = child_capacity,
                     capWording = child_capWording)) %>%
  full_join(efa_comb_small_rot_loadings %>%
              setNames(paste0('comb_', names(.))) %>%
              rename(capacity = comb_capacity, 
                     capWording = comb_capWording)) %>%
  arrange(comb_factor, desc(comb_loading_abs)) %>%
  mutate(capWording = gsub("_", " ", capWording)) %>%
  select(capWording, 
         adult_F2, child_F1, comb_F1, # double-check adults!
         adult_F1, child_F2, comb_F2, # double-check adults!
         adult_F3, child_F3, comb_F3) %>%
  column_to_rownames(var = "capWording") %>%
  signif(3) %>%
  round(2)

big_table
```

```{r relationships between scores}
cor.plot(scores_all %>% column_to_rownames(var = "subid") %>% select(-ageGroup))
cor.test(scores_all$score_F1, scores_all$score_F2, use = "complete.obs")
cor.test(scores_all$score_F1, scores_all$score_F3, use = "complete.obs")
cor.test(scores_all$score_F2, scores_all$score_F3, use = "complete.obs")


cor.ci(scores_adults %>% column_to_rownames(var = "subid") %>% select(-ageGroup))
cor.ci(scores_children %>% column_to_rownames(var = "subid") %>% select(-ageGroup))
cor.ci(scores_all %>% column_to_rownames(var = "subid") %>% select(-ageGroup))
```

```{r new plot}
# by condition
d1_bycond3 <- d2 %>%
  left_join(d_child02 %>% select(subid, age)) %>%
  select(character, capacity, capWording, responseCat, subid, ageGroup, age) %>%
  filter(capacity != "na", !is.na(responseCat)) %>%
  mutate(capWording = gsub(" --.*", "", capWording),
         ageGroup3 = ifelse(ageGroup == "adult", "adult",
                            ifelse(is.na(age), NA,
                                   ifelse(age < 8, "7y", 
                                          ifelse(age < 9, "8y",
                                                 ifelse(age < 10, "9y",
                                                        NA)))))) %>%
  distinct()

d1_bycond3 %>% select(ageGroup3, subid) %>% distinct() %>% count(ageGroup3)

# make df for plotting
d1_bycond3_mb_factorsAll <- d1_bycond3 %>% 
  full_join(efa_comb_small_rot_loadings %>%
              group_by(factor) %>%
              mutate(order = rank(desc(loading_abs)))) %>%
  arrange(character, factor, desc(loading_abs)) %>%
  rownames_to_column(var = "full_order") %>%
  mutate(full_order = as.numeric(full_order)) %>%
  arrange(factorName, full_order) %>%
  ungroup() %>%
  filter(!is.na(ageGroup3)) %>%
  mutate(factorName = factor(factorName,
                             levels = c("Factor 1", "Factor 2", "Factor 3"),
                             labels = c("Social-emotional", 
                                        "Physiological", 
                                        "Perceptual-cognitive")),
         agentic = ifelse(capWording %in% c("decide_what_to_do", "make_choices", "have_self_control", 
                                            "make_plans", "have_goals", 
                                            "understand_how_somebody_else_is_feeling",
                                            "know_what's_nice_and_what's_mean", "have_thoughts",
                                            "remember_things", "communicate_with_somebody_else"),
                          TRUE, FALSE))


d1_bycond3_annotate <- d1_bycond3_mb_factorsAll %>% 
  filter(order <= 4) %>% 
  select(character, order, capWording) %>% 
  distinct()

# ggplot(d1_bycond3_mb_factorsAll %>% filter(order <= 4), 
#        aes(x = interaction(ageGroup3, desc(order*2)), 
#            # y = responseCat,
#            # color = ageGroup3, shape = ageGroup3,
#            fill = responseCat,
#            label = ageGroup3)) +
#            # label = gsub("_", " ", paste(capWording, ageGroup3, sep = ": ")))) +
#   # facet_grid(factorName ~ character * order, scales = "free", space = "free") +
#   facet_grid(factorName ~ character, scales = "free", space = "free") +
#   geom_bar(position = "fill") +
#   geom_text(aes(y = -0.5, hjust = 0), 
#             color = "black",
#             # color = d1_bycond2_mb_factorsAll$textColor,
#             size = 8) +
#   geom_text(aes(x = order, y = 1.5, label = capWording), hjust = -1) +
#   # annotate("text", aes(facet = order, label = d1_bycond3_annotate$capWording)) +
#   labs(title = "Responses by mental capacity item",
#        y = "Relative frequency",
#        x = "Capacity * Age group",
#        fill = "Response: ") +
#   scale_y_continuous(breaks = c(0, 0.5, 1)) +
#   # coord_flip() +
#   theme_bw() +
#   theme(text = element_text(size = 28),
#         # axis.title.y = element_blank(),
#         axis.text.y = element_blank(),
#         axis.ticks.y = element_blank(),
#         legend.position = "top") # 1700 by 2000

ggplot(d1_bycond3_mb_factorsAll %>% 
         filter(order <= 5) %>%
         mutate(facet = tools::toTitleCase(paste0(character, " (", ageGroup, ")"))), 
         # mutate(facet = tools::toTitleCase(paste(ageGroup, character, sep = ": "))), 
       aes(x = desc(order*2), 
           fill = character,
           alpha = responseCat,
           # fill = responseCat,
           label = gsub("_", " ", capWording))) +
  facet_grid(factorName ~ facet) + #, scales = "free", space = "free") +
  geom_bar(position = "stack", color = "black") +
  scale_alpha_manual(values=c(0.4, 0.7, 1)) +
  geom_text(aes(y = 5, hjust = 0), 
            color = "black",
            # color = d1_bycond2_mb_factorsAll$textColor,
            size = 8) +
  labs(title = "Responses by mental capacity item",
       y = "Count",
       x = "Capacity",
       alpha = "Response: ",
       fill = "Character: ") +
  coord_flip() +
  theme_bw() +
  theme(text = element_text(size = 28),
        # axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        legend.position = "top") # 2000 by 1200

ggplot(d1_bycond3_mb_factorsAll %>%
         # filter(order <= 10) %>%
         # filter(order <= 5 | capWording %in% c("know_what's_nice_and_what's_mean",
         #                                       "make_choices")) %>% # add selected agency by factor
         mutate(facet = tools::toTitleCase(paste0(character, " (", ageGroup, ")"))), 
         # mutate(facet = tools::toTitleCase(paste(ageGroup, character, sep = ": "))), 
       aes(x = desc(order*2), 
           fill = character,
           alpha = responseCat,
           # fill = responseCat,
           label = gsub("_", " ", capWording))) +
  facet_grid(factorName ~ facet, scales = "free", space = "free") +
  geom_bar(position = "stack", color = "black") +
  scale_alpha_manual(values=c(0.4, 0.7, 1)) +
  geom_text(aes(y = 5, hjust = 0), 
            color = "black",
            # color = d1_bycond2_mb_factorsAll$textColor,
            size = 8) +
  labs(title = "Responses by mental capacity item",
       y = "Count",
       x = "Capacity",
       alpha = "Response: ",
       fill = "Character: ") +
  coord_flip() +
  theme_bw() +
  theme(text = element_text(size = 32),
        # axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        legend.position = "top") # 2000 by 1200

```

```{r srcd plot version 1}
d_srcd <- d1_bycond3_mb_factorsAll %>% 
         filter(factor == "F1", order <= 5) %>%
  select(ageGroup, character, responseCat, order, capWording) %>%
  mutate(labeller = ifelse(responseCat == "no", gsub("_", " ", capWording), NA)) %>%
  distinct()
  
ggplot(d1_bycond3_mb_factorsAll %>% 
         filter(factor == "F1", order <= 5) %>%
         mutate(facet = tools::toTitleCase(paste(ageGroup, character, sep = ": "))),
       aes(x = desc(order*2), 
           fill = character,
           alpha = responseCat,
           label = gsub("_", " ", capWording))) +
  facet_grid(factorName ~ facet) + #, scales = "free", space = "free") +
  geom_bar(position = "stack", color = "black") +
  scale_alpha_manual(values=c(0.2, 0.5, 1)) +
  # scale_alpha_manual(values=c(0.4, 0.7, 1)) +
  geom_text(data = d_srcd, 
            aes(x = desc(order*2), label = labeller, y = 5),
            hjust = 0, angle = 90, color = "black", size = 8, alpha = 1) +
  labs(title = "Responses by mental capacity item",
       y = "Count",
       x = "Capacity",
       alpha = "Response: ",
       fill = "Character: ") +
  theme_bw() +
  theme(text = element_text(size = 28),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "bottom") # 1000 by 500
```

```{r srcd plot version 2}
d_srcd2 <- d1_bycond3_mb_factorsAll %>% 
         filter(factor == "F1", order <= 5, character == "robot") %>%
  select(ageGroup, character, responseCat, order, capWording) %>%
  mutate(labeller = ifelse(responseCat == "no", gsub("_", " ", capWording), NA)) %>%
  distinct()
  
ggplot(d1_bycond3_mb_factorsAll %>% 
         filter(factor == "F1", order <= 5) %>%
         mutate(facet = tolower(paste(ageGroup, character, sep = ": "))) %>%
         mutate(facet = factor(facet, 
                               levels = c("child: beetle", "child: robot", 
                                          "adult: beetle", "adult: robot"),
                               labels = c("children: beetle", "children: robot", 
                                          "adults: beetle", "adults: robot"))) %>%
         mutate(ageGroup2 = factor(ageGroup, levels = c("child", "adult"),
                                   labels = c("children", "adults"))),
       aes(x = desc(order*2), 
           fill = character,
           alpha = responseCat,
           label = gsub("_", " ", capWording))) +
  facet_grid(~ facet) + #, scales = "free", space = "free") +
  geom_bar(position = "stack", color = "black") +
  scale_alpha_manual(values=c(0.2, 0.5, 1)) +
  # scale_alpha_manual(values=c(0.4, 0.7, 1)) +
  geom_text(data = d_srcd2, 
            aes(x = desc(order*2), label = labeller, y = 5),
            hjust = 0, angle = 90, color = "black", size = 8, alpha = 1) +
  labs(y = "Count",
       x = "Capacity",
       alpha = "Response: ",
       fill = "Character: ") +
  labs(title = "Responses (raw counts)",
       y = "Count",
       x = "Capacity",
       alpha = "response: ",
       fill = "character: ") +
  theme_bw() +
  theme(text = element_text(size = 28),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "bottom") # 1000 by 500

ggplot(d1_bycond3_mb_factorsAll %>% 
         filter(factor == "F1", order <= 5, character == "robot") %>%
         mutate(facet = tools::toTitleCase(paste(ageGroup, character, sep = ": "))) %>%
         mutate(facet = factor(facet, 
                               levels = c("Child: Robot", "Adult: Robot"),
                               labels = c("Children: robot", "Adults: robot"))) %>%
         mutate(ageGroup2 = factor(ageGroup, levels = c("child", "adult"),
                                   labels = c("children", "adults"))),
       aes(x = desc(order*2), 
           alpha = responseCat,
           label = gsub("_", " ", capWording))) +
  facet_grid(~ ageGroup2) + #, scales = "free", space = "free") +
  geom_bar(position = "stack", color = "black", fill = "#00BFC4") +
  scale_alpha_manual(values=c(0.2, 0.5, 1)) +
  # scale_alpha_manual(values=c(0.4, 0.7, 1)) +
  geom_text(data = d_srcd2, 
            aes(x = desc(order*2), label = labeller, y = 5),
            hjust = 0, angle = 90, color = "black", size = 8, alpha = 1) +
  labs(y = "Count",
       x = "Capacity",
       alpha = "Response: ",
       fill = "Character: ") +
  theme_bw() +
  theme(text = element_text(size = 28),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        legend.position = "bottom") # 600 by 500
```